System and method for determining predicted lifetime of component

Abstract

To provide a system and a method for determining a predicted lifetime of a component.SOLUTION: A method includes a step of processing an image of a component in order to detect a particle structure on the component by a computing system including one or more processors. The method further includes a step of comparing the detected particle structure with a stress map of the component by the computing system. The method further includes a step of determining a predicted lifetime of the component on the basis of localization of the detected particle structure and the stress map by the computing system.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure generally relates to systems and methods for determining the predicted life of a component, such as, in some embodiments, a turbine component. [Background technology]

[0002] Across a variety of applications, consistent and accurate predictions of component life are generally desirable, as such predictions can increase efficiency by reducing damage from component failure and enabling improved planning for component decommissioning.

[0003] One application where such consistent and accurate predictions are desirable is in applications where components are subjected to numerous extreme conditions (e.g., high temperatures, high pressures, large stress loads, etc.). Over time, individual components of the equipment may experience creep, deformation, fatigue cracking, etc., which may shorten the component's usable life. Such concerns may apply, for example, to some turbomachines, such as gas turbine systems. During operation of the turbomachine, various components within the turbomachine, particularly within the turbine section of the turbomachine, such as turbine blades (collectively known as turbine components), may experience creep due to high temperatures and stresses. In the case of turbine blades, creep may cause some or all of the blades to elongate, causing the blade tips to contact stationary structures, such as the turbine casing, resulting in undesirable vibrations and / or performance degradation during operation. Furthermore, excessive creep may cause creep rupture and resulting component failure, which may result in unplanned shutdowns and damage to other components within the system.

[0004] Thus, components such as turbine components can be monitored for creep. One approach to monitoring components for creep is to configure the components with strain sensors and analyze the strain sensors at various intervals to monitor deformation associated with creep strain. One drawback of such an approach is that the device for analyzing the strain sensors must be positioned in a specific location relative to the strain sensors during each analysis of the strain sensors to prevent any errors due to misalignment from being introduced into the deformation analysis. This positioning is time-consuming and expensive, and therefore can create inefficiencies in the deformation monitoring process.

[0005] Therefore, improved systems and methods for predicting component life are desirable. For example, systems and methods that can consistently and accurately predict creep, thereby enabling predictive life planning based on such predictions, would be advantageous. Summary of the Invention

[0006] Aspects and advantages of the systems and methods according to the present disclosure will be set forth in part in the description that follows, or may be obvious from the description, or may be learned by practice of the techniques.

[0007] According to one embodiment, a method for determining a predicted life of a component is provided. The method includes processing, by a computing system having one or more processors, an image of the component to detect grain structure on the component. The method further includes comparing, by the computing system, the detected grain structure to a stress map of the component. The method further includes determining, by the computing system, a predicted life of the component based on the localization of the detected grain structure and the stress map.

[0008] According to another embodiment, a computing system for determining a predicted life of a component is provided. The system includes one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations include processing an image of the component to detect grain structure on the component, comparing the detected grain structure to a stress map of the component, and determining a predicted life of the component based on the localization of the detected grain structure and the stress map.

[0009] These and other features, aspects, and advantages of the present systems and methods will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present technology and, together with the description, serve to explain the principles of the technology.

[0010] A full and enabling disclosure of the present systems and methods, including the best mode thereof, directed to one of ordinary skill in the art, of making and using the same, is set forth in this specification, which makes reference to the accompanying figures. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram of a system for acquiring an image of a component according to an embodiment of the present disclosure. [Figure 2] FIG. 10 illustrates a video of a component according to an embodiment of the present disclosure. [Figure 3] FIG. 1 illustrates multiple images of a component according to an embodiment of the present disclosure. [Figure 4] FIG. 1 illustrates processing of an image of a component by cropping the image according to an embodiment of the present disclosure. [Figure 5] FIG. 1 illustrates processing of an image of a component by pixel analysis of the image according to an embodiment of the present disclosure. [Figure 6] FIG. 1 is a schematic diagram of a detected grain structure on a component, according to an embodiment of the present disclosure. [Figure 7] FIG. 10 illustrates a comparison between detected grain structure and a stress map of a component, according to an embodiment of the present disclosure. [Figure 8] FIG. 1 illustrates a computing system according to an embodiment of the present disclosure. [Figure 9] 1 is a flowchart illustrating a method for determining a predicted life of a component, according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] Reference will now be made in detail to the present system and method embodiments, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation, not limitation, of the technology. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the technology without departing from the scope or spirit of the claimed technology. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield yet a further embodiment. Accordingly, the present disclosure is intended to cover such modifications and variations as come within the scope of the appended claims and their equivalents.

[0013] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. Moreover, unless otherwise specified, all embodiments described herein are to be considered exemplary.

[0014] The detailed description uses numerical and letter designations to refer to features in the drawings. Like or similar designations in the drawings and description are used to refer to like or similar parts of the invention. As used herein, the terms "first," "second," and "third" may be used interchangeably to distinguish one component from another and are not intended to denote the location or importance of individual components.

[0015] The term "fluid" may be a gas or a liquid. The term "fluid communication" means that a fluid is capable of making a connection between designated areas.

[0016] As used herein, the terms "upstream" (or "forward") and "downstream" (or "aft") refer to relative directions with respect to fluid flow in a fluid pathway. For example, "upstream" refers to the direction from which fluid flows, and "downstream" refers to the direction from which fluid flows. However, as used herein, the terms "upstream" and "downstream" can also refer to electrical flow. The term "radially" refers to a relative direction substantially perpendicular to the axial centerline of a particular component, the term "axially" refers to a relative direction substantially parallel to and / or coaxially aligned with the axial centerline of a particular component, and the term "circumferentially" refers to a relative direction extending around the axial centerline of a particular component.

[0017] Approximate terms such as "approximately," "about," "generally," and "substantially" are not intended to be limited to the exact value stated. In at least some cases, approximating language can correspond to the precision of an instrument for measuring a value or the precision of a method or machine for constructing or manufacturing a component and / or system. In at least some cases, approximating language can correspond to the precision of an instrument for measuring a value or the precision of a method or machine for constructing or manufacturing a component and / or system. For example, approximating language can refer to within a margin of 1, 2, 4, 5, 10, 15, or 20% of a particular value, a range of values, and / or any of the endpoints defining the range of values. When used in the context of angles or directions, such terms include a range of 10 degrees greater or less than the stated angle or direction. For example, "generally vertical" includes any direction, e.g., clockwise or counterclockwise, within 10 degrees of vertical.

[0018] Terms such as "coupled," "fixed," and "attached," unless expressly stated otherwise herein, refer to both direct coupling, fixing, or attachment, and indirect coupling, fixing, or attachment via one or more intermediate components or features. As used herein, the terms "comprises," "comprising," "includes," "including," "has," and "having," or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that includes a list of features is not necessarily limited to only those features but may include other features not expressly listed or that are inherent to such process, method, article, or apparatus. Furthermore, unless expressly stated to the contrary, "or" refers to an inclusive or, not an exclusive or. For example, condition A or B can be satisfied by any one of A being true (or present) and B being false (or absent), A being false (or absent) and B being true (or present), and both A and B being true (or present).

[0019] Here, and throughout the specification and claims, range limitations are combinable and interchangeable, and unless the context and language dictate otherwise, such ranges are identified and include all subranges subsumed therein. For example, all ranges disclosed herein are inclusive of the endpoints, and the endpoints are independently combinable with each other.

[0020] Referring now to FIG. 1 , a component 10 is provided. Component 10 (more particularly, the substrate of the entire component 10) may include various types of components used in a variety of different applications, such as, for example, components utilized in high-temperature applications (e.g., components including nickel- or cobalt-based superalloys, austenitic steels, etc.). In some embodiments, component 10 may include an industrial gas turbine or steam turbine component, such as a combustion component or a hot gas path component. In some embodiments, component 10 may include a turbine blade, compressor blade, vane, nozzle, shroud, rotor, transition piece, or casing. In other embodiments, component 10 may include any other component of a turbine, such as any other component for a gas turbine, steam turbine, etc. In some embodiments, the components may include non-turbine components, including, but not limited to, automotive components (e.g., cars, trucks, etc.), aerospace components (e.g., aircraft, helicopters, space shuttles, aluminum parts, etc.), locomotive or railroad components (e.g., trains, train tracks, etc.), structural, infrastructure, or civil engineering components (e.g., bridges, buildings, construction equipment, etc.), and / or power generation equipment or chemical processing components (e.g., pipes used in high temperature applications).

[0021] In an exemplary embodiment, component 10 is an equiaxed or directionally solidified component. For example, component 10 may be a cast component, and after casting, the melt in the mold may advantageously become equiaxed or directionally solidified.

[0022] 1 also shows a coordinate system, which includes an X-axis 50, a Y-axis 52, and a Z-axis 54, all of which are mutually orthogonal and defined relative to the component 10.

[0023] 1 further illustrates a computing system 100, which may include, for example, a data collection system 102 and a user computing system 104. The data collection system 102 generally collects data regarding the component 10, and the computing system 104 generally analyzes the data and performs various calculations and other functions as described herein. In particular, the computing system 100 according to the present disclosure provides accurate and efficient predictions of the lifespan of the component 10, as described herein.

[0024] It should be noted that the various subsystems within computing system 100, such as data collection system 102, user computing system 104, and other suitable subsystems, may be linked together as described herein or may be separate, discrete systems.

[0025] According to one embodiment, the data collection system 102 may include an imaging device 106 for acquiring one or more images of the component 10. Such images may be in the form of discrete images (e.g., photographs) or videos including multiple video frame images extracted from a video. For example, the imaging device 106 may include a lens assembly 110 and an image capture device 112. The lens assembly 110 may generally magnify an image viewed by the lens assembly 110 for processing by the image capture device 112. The lens assembly 110 in some embodiments may be, for example, a suitable camera lens, a telescope lens, or the like, and may include one or more lenses spaced apart to provide the necessary magnification. The image capture device 112 may generally be in communication with the lens assembly 110 to receive and process light from the lens assembly 110 to generate an image. In an exemplary embodiment, for example, the image capture device 112 may be a camera sensor that receives and processes light from a camera lens to generate an image, such as a digital image, as commonly understood. Image capture device 112 (and device 106 generally) may, in some embodiments, further be in communication with user computing system 104, e.g., via a suitable wired or wireless connection, for storing and analyzing images from image capture device 112 and device 106 generally. In some embodiments, user computing system 104 may operate imaging device 106 to perform the various disclosed steps. In other embodiments, imaging device 106 may be a standalone device separately operated by a user, may be linked to user computing system 104, or may be a separate, discrete system.

[0026] Additionally or alternatively, the data collection system 102 may further include a three-dimensional data collection device 108 for inspecting the exterior surface 11 of the component 10. The device 108 according to the present disclosure generally utilizes surface metrology techniques to obtain direct measurements of the component 10 along three axes. In particular, in exemplary embodiments, non-contact surface metrology techniques may be utilized. In general, any suitable three-dimensional data collection device 108 that utilizes surface metrology techniques to obtain direct measurements in three dimensions may be utilized. In exemplary embodiments, the device 108 is a non-contact device that utilizes non-contact surface metrology techniques.

[0027] According to one embodiment, the device 108 in some exemplary embodiments is a laser scanner that generates a laser scanned image. A laser scanner generally includes a laser 120 that emits light in the form of a laser beam toward an object, such as component 10 in these embodiments. The light is then detected by a sensor 124 of the device 108. For example, in some embodiments, the light is reflected from a surface it contacts and received by the sensor 124 of the device 108. The round-trip time for the light to reach the sensor 124 is utilized to determine measurements along various axes. These devices are commonly known as time-of-flight devices. In other embodiments, the sensor 124 detects light on a surface it contacts and determines measurements based on the relative position of the light within the field of view of the sensor 124. These devices are commonly known as triangulation devices. X-, Y-, and Z-axis data points are calculated based on the detected light, as described above.

[0028] In some embodiments, the light emitted by laser 120 is emitted in a band wide enough to reflect off of a portion of the object being measured. In these embodiments, a robotic arm (as described herein) or other suitable mechanism for moving laser 120 may be utilized to move laser 120 and the emitted band as needed until the light is reflected off of the entire object being measured.

[0029] In other embodiments, other suitable surface metrology devices may be utilized. For example, in some embodiments, device 108 may be an X-ray scanner that provides images in the form of X-rays. In some embodiments, device 108 may be or include a high-resolution crystal orientation system that provides images in the form of Laue diffraction patterns, e.g., Laue orientation images. In some embodiments, device 108 may be a scanning electron microscope that provides images in the form of electron channeling contrast images. In some embodiments, device 108 may be a three-dimensional scanner that provides images in the form of three-dimensional scanned geometries.

[0030] In some embodiments, the data collection system 102 may include a robotic arm 130. The robotic arm 130 may support and facilitate movement of other components of the data collection system 102 relative to the component to acquire images of the component. For example, the imaging device 106 and the data collection device 108 (or components thereof, such as a light source) may be mounted to the robotic arm 130. Movement of the robotic arm 130 may position the data collection system 102 or its components (such as a light source) relative to the component 10, in exemplary embodiments. In some embodiments, other components, such as the imaging device 106, may remain fixed, while components such as the light source are movable. In the exemplary embodiment, the robotic arm 130 is a six-degree-of-freedom arm 130 that provides movement along and about axes 50, 52, and 54.

[0031] In some embodiments, the user computing system 104 may operate the data collection system 102 to perform the various disclosed steps. In other embodiments, the data collection system 102 may be a standalone device operated separately by a user, may be linked to the user computing system 104, or may be a separate, discrete system. For example, in some embodiments, the user may manually capture images and upload them to the user computing system 104.

[0032] Figure 2 shows an exemplary embodiment of a plurality of images 200, in the form of a video, in this embodiment including a plurality of video frames. Figure 3 shows a plurality of images 200, which may be a plurality of video frames from Figure 2, a plurality of photographs, or a plurality of images obtained from another suitable embodiment of data collection system 102 described above. Note that such images may be provided by data collection system 102 and generally computing system 100, or independently of data collection system 102 and generally computing system 100.

[0033] A computing system 100, such as data acquisition system 102 and / or its user computing system 104, may be capable of processing one or more images 200 of component 10. Such processing may detect one or more grain structures on component 10.

[0034] Grain structure according to the present disclosure may be or include one or more of the following: grains, grain boundaries, grain or grain boundary size (e.g., length, etc.), particle shape factor, minimum, maximum, median, etc., aggregate grain size, grain or grain boundary orientation, and / or grain boundary triple points.

[0035] For example, in some embodiments, such processing may include cropping one or more images 200. Cropping allows for focusing on a particular area of ​​interest on component 10, such as a portion of the component that is particularly susceptible to high temperatures. Figure 4 shows image 200 with a cropped portion 202 of it.

[0036] Additionally or alternatively, in some embodiments, such processing may include performing pixel analysis of image 200. This analysis generally distinguishes a reference object (e.g., a grain structure) from a background (e.g., a component surface and background) based on differences in color depth (i.e., differences in color or grayscale). The analysis may be performed for each individual pixel or group of pixels defining image 200. To perform the pixel analysis, the number of bits per pixel of the image, i.e., 128, 256, etc., may be divided into, for example, two or more groups (e.g., a group including a lighter color depth and a group including a darker color depth). Each group is classified as a reference object portion or a background portion. For example, the color depth analysis may classify pixels or multi-pixel groups with a darker or lighter color depth as indicative of a reference object (i.e., a surface feature versus a component, or a component versus a background), and classify pixels or multi-pixel groups with the other of the darker or lighter color depth as indicative of a background (i.e., a component versus a surface feature, or a background versus a component). In particular, different divisions into lighter and darker groups can be utilized to distinguish surface features from components and components from background.

[0037] FIG. 5 shows an embodiment of pixel analysis in which image contrast is gradually improved through pixel analysis so that grain structures are detected.

[0038] In some embodiments, computer vision is utilized to perform image cropping and / or pixel analysis. An example of computer vision software suitable for such analysis is, for example, the OpenCV Python library.

[0039] 6 illustrates multiple detected grain structures 210 on component 10. In particular, the detected grain structures may be detected relative to axes 50, 52, 54 in an exemplary embodiment. Understanding the localization (e.g., orientation, location, and / or shape) of the detected grain structures is important for creep and life prediction according to the present disclosure.

[0040] The computing system 100, such as the data acquisition system 102 and / or its user computing system 104, may further be capable of comparing the detected grain structure 210 to a stress map 220 of the component 10. This advantageously facilitates correlation of structural details with directional stresses in specific regions. The stress map 220 may include a map of stress directions, such as a contour map of various stress tensor components in one or more directions, such as along axes 50, 52, and / or 54, and along directions at various angles relative to axes 50, 52, and / or 54. The stress map 220 may be generated, for example, by a finite element analysis (“FEA”) of the component 10, which may be performed, for example, by the computing system 100 or may be performed separately (e.g., via a separate computing system) and provided to the computing system. Examples of FEA software suitable for such analysis include, for example, ANSYS, Simulia, Nastran, etc.

[0041] Comparing the detected grain structure 210 with the stress map 220 may allow for determination of where the grain structure and regions of stress increase or stress direction occur. Furthermore, such a comparison may allow for a comparison between the localization of such grain structure (e.g., relative to the axes 50, 52, 54) and the direction of stress increase or stress direction.

[0042] 7(A), 7(B), 7(C), and 7(D), in some embodiments, the comparing step may include overlaying the stress map 220 onto the processed image 200 to compare the localization of the detected grain structure 210 with the stress directions 222 (e.g., directional orientation of stress concentrations) of the stress map 220. This overlay may be performed with reference to the axes 50, 52, 54 so that the axes in the processed image 200 and the stress map 220 coincide.

[0043] Such a comparison may include, for example, detecting grain structures 210 within the stress direction 222 and determining the localization of those detected grain structures 210 relative to a particular stress direction 222. For example, FIGS. 7(A) and 7(B) show the overlay of a stress map 220 onto a processed image 200 to compare the localization of the detected grain structures 210 with the stress direction 222 along the X-axis 50. As shown in FIG. 7(A), an arrow 230 indicates an exemplary grain structure 210 that is within the stress direction 222 and generally perpendicular to the stress direction 222. As shown in FIG. 7(B), an arrow 232 indicates an exemplary grain structure 210 that is within the stress direction 222 and generally parallel to the direction of the stress direction 222. FIGS. 7(C) and 7(D) show the overlay of a stress map 220 onto a processed image 200 to compare the localization of the detected grain structures 210 with the stress direction 222 along the Y-axis 52. As shown in Figure 7(C), arrow 234 indicates an exemplary grain structure 210 that is in and generally perpendicular to stress direction 222. As shown in Figure 7(D), arrow 236 indicates an exemplary grain structure 210 that is in and generally parallel to stress direction 222.

[0044] The computing system 100, such as the data collection system 102 and / or its user computing system 104, may further be capable of determining a predicted lifespan of the component 10 based on, for example, the creep behavior of the material. The predicted lifespan of the component 10 may be based on the localization of one or more detected grain structures 210 and the stress map 220 of the component 10. For example, such a determination may be based on detecting grain structures 210 that lie within a stress direction 222 and determining the localization of those detected grain structures 210 relative to a particular, e.g., stress direction 222.

[0045] In an exemplary embodiment, the determining step includes determining a creep probability based on the localization of one or more detected grain structures 210 and the stress directions 222 of the stress map 220, and determining a predicted life of the component 10 based on the creep probability. For example, detection of grain structures 210 within the stress directions 222 and determination of the localization of those detected grain structures 210 relative to particular orientations of the stress directions 222 may be utilized to determine a creep probability of the component 10. One or more creep probabilities may be generated based on such detection and determination. The creep probability may then be utilized to adjust the predicted life of the component 10, such as relative to a baseline predicted life.

[0046] For example, as will be explained, FIG. 7(A) shows an exemplary grain structure 210 that is in the stress direction 222 and generally perpendicular to the stress direction 222. FIG. 7(C) also shows an exemplary grain structure 210 that is in the stress direction 222 and generally perpendicular to the stress direction 222. Such a generally perpendicular grain structure 210 increases the probability of creep and therefore reduces the expected life of the component 10. FIG. 7(B) shows an exemplary grain structure 210 that is in the stress direction 222 and generally parallel to the stress direction 222. FIG. 7(D) also shows an exemplary grain structure 210 that is in the stress direction 222 and generally parallel to the stress direction 222. Such a generally parallel grain structure 210 may reduce or not increase the probability of creep, or increase it relatively less than perpendicular, for example, and therefore may increase or not decrease the expected life of the component 10.

[0047] It should be understood that the present disclosure is not limited to comparisons with stress directions only along axes 50, 52, and / or 54, or to predictive life determinations based only on generally parallel or generally perpendicular grain structures. Rather, such comparisons and determinations are exemplary embodiments, and the present disclosure encompasses predictive life determinations based on stress directions in any suitable direction and on grain structures having any suitable localization relative to the stress directions. The inventors have discovered the ability to detect macrostructures on actual three-dimensional components using nondestructive characterization techniques and to link the extracted grain structures with stress maps to perform automated computer-based inferences regarding the creep life of a particular component. The present disclosure advantageously facilitates more accurate and consistent life predictions and creep probability determinations based thereon.

[0048] In some embodiments, additional variables may be utilized to determine the expected life of component 10. For example, such additional variables may be utilized to determine creep probability. Examples of such variables may include, for example, the material type of the component, the age of component 10, and / or the temperature at which component 10 is used.

[0049] The grain structure, or the value or magnitude of one or more of such additional variables, may be utilized to adjust the predicted life. For example, a relatively large grain structure (e.g., relative to a predetermined threshold value for the component 10) may increase the predicted life, and a relatively small grain structure (e.g., relative to a threshold value) may decrease the predicted life. Similarly, a relatively short usage time or low usage temperature (e.g., relative to a predetermined threshold value for the component 10) may increase the predicted life, and a relatively long usage time or high usage temperature (e.g., relative to a threshold value) may decrease the predicted life. Such increases and / or decreases may be made relative to a baseline predicted life for the component, which may be predetermined or determined using the computing system 100.

[0050] The values ​​or magnitudes of such additional variables, as well as thresholds, may be determined by computing system 100 or may be determined independently and provided to computing system 100 for use in the determining step.

[0051] The determined predicted lifespan may advantageously be output from computing system 100, thereby allowing a user of the computing system to receive the determined predicted lifespan. The predicted lifespan determination may advantageously provide a relatively accurate lifespan estimate, thus enabling a user to extend the actual useful lifespan of component 10 as allowed in accordance with the determined predicted lifespan, while reducing concerns about the risk of failure. The predicted lifespan determination may further advantageously enable a user to shorten the actual useful lifespan of component 10 and decommission component 10 before risk of failure, etc., in accordance with the determined predicted lifespan, thus reducing unplanned shutdown issues.

[0052] 8 illustrates a block diagram of an exemplary computing system 100 that performs one or more of the various functions and steps described herein, according to embodiments of the present disclosure. The computing system 100 may include multiple computing systems that communicate over a network 1680. The computing system 100 may include one or more user computing systems 104 and one or more data collection systems 102.

[0053] The user computing system 104 may include one or more computing devices. The computing devices may include a mobile computing device (e.g., a smartphone or tablet), a laptop computing device, a desktop computing device, a wearable computing device (e.g., a smart watch, a smart jacket, smart glasses, a smart backpack, etc.), a smart appliance (e.g., a smart thermostat, a smart refrigerator, a smart washer, a smart dryer, etc.), an embedded computing device, a surveillance computing device (e.g., a drone), or any other type of computing device.

[0054] The user computing system 104 may include one or more processors 1612 that may be utilized to perform one or more operations. The one or more processors 1612 may include any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or multiple processors operably connected. The one or more processors 1612 may perform operations serially and / or in parallel. The one or more processors 1612 may be dedicated to a particular computing device and / or may be utilized by multiple devices to perform processing tasks.

[0055] The user computing system 104 may include memory 1614 that can store data 1616 and / or instructions 1618. The memory 1614 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The data 1616 may include user data, application data, operating system data, etc. The data 1616 may include text data, image data, audio data, statistical data, latent coding data, etc. The instructions 1618 may include instructions that, when executed by the one or more processors 1612, cause the user computing system 104 to perform operations.

[0056] In some implementations, the user computing system 104 can store and utilize one or more machine learning models 1620. The one or more machine learning models 1620 can include computer vision models, which can include, for example, object detection models. The one or more machine learning models 1620 can further include detection models, natural language processing models, segmentation models, classification models, augmentation models, generative models, discriminative models, and / or one or more other model types. In some implementations, the one or more machine learning models 1620 can include various machine learning models, such as neural networks (e.g., deep neural networks), or other types of machine learning models, including nonlinear and / or linear models. The neural networks can include convolutional neural networks, feedforward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), or other forms of neural networks. The one or more machine learning models 1620 can include one or more Transformer models.

[0057] The user computing system 104 may include one or more user input components 1622, one or more user interfaces 1624, and / or one or more sensors 1626. The one or more user input components 1622 may be configured to receive user input and / or environmental input. For example, the one or more user input components 1622 may include a touch-sensitive component (e.g., a touch-sensitive display or touchpad) that responds to the touch of a user input object (e.g., a finger or stylus). The touch-sensitive component may be useful for implementing a virtual keyboard. Other exemplary user input components include a computer mouse, a remote, a controller, a microphone, a traditional keyboard, or other means by which a user can provide user input. In some implementations, the one or more user input components 1622 may include one or more gesture processing engines for determining touch gestures, audio gestures, and / or physical gestures. The one or more user interfaces 1624 may be configured to acquire and / or display data. The one or more user interfaces 1624 may be associated with an operating system, one or more applications, one or more web platforms, and / or one or more devices. The one or more sensors 1626 may include one or more image sensors, one or more infrared sensors, one or more light detection and ranging (lidar) sensors, one or more audio sensors, one or more touch sensors, one or more sonic navigation and ranging (sonar) sensors, and / or one or more thermal sensors.

[0058] The user computing system 104 may be communicatively connected to the data collection system 102 via a network 1680, which may include the Internet (e.g., Ethernet and / or WiFi), Bluetooth, and / or direct wiring.

[0059] The data collection system 102 may include one or more computing devices. The computing devices may include a mobile computing device (e.g., a smartphone or tablet), a laptop computing device, a desktop computing device, a wearable computing device (e.g., a smart watch, a smart jacket, smart glasses, a smart backpack, etc.), a smart appliance (e.g., a smart thermostat, a smart refrigerator, a smart washer, a smart dryer, etc.), an embedded computing device, a surveillance computing device (e.g., a drone), or any other type of computing device.

[0060] The data collection system 102 may include one or more processors 1712 that may be utilized to perform one or more operations. The one or more processors 1712 may include any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, an FPGA, a controller, a microcontroller, etc.) and may be one processor or multiple processors operably connected. The one or more processors 1712 may perform operations serially and / or in parallel. The one or more processors 1712 may be dedicated to a particular computing device and / or may be utilized by multiple devices to perform processing tasks.

[0061] The data collection system 102 may include memory 1714 that can store data 1716 and / or instructions 1718. The memory 1714 may include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof. The data 1716 may include user data, application data, operating system data, etc. The data 1716 may include text data, image data, audio data, statistical data, latent coding data, etc. The instructions 1718 may include instructions that, when executed by the one or more processors 1712, cause the user computing system 104 to perform an operation.

[0062] In some implementations, the data collection system 102 can store and utilize one or more machine learning models 1720. The one or more machine learning models 1720 can include computer vision models, which can include, for example, object detection models. The one or more machine learning models 1720 can further include detection models, natural language processing models, segmentation models, classification models, augmentation models, generative models, discriminative models, and / or one or more other model types. In some implementations, the one or more machine learning models 1720 can include various machine learning models, such as neural networks (e.g., deep neural networks), or other types of machine learning models, including nonlinear and / or linear models. The neural networks can include convolutional neural networks, feedforward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), or other forms of neural networks. The one or more machine learning models 1720 can include one or more Transformer models.

[0063] The data collection system 102 may include one or more user input components 1722, one or more user interfaces 1724, and / or one or more sensors 1726. The one or more user input components 1722 may be configured to receive user input and / or environmental input. For example, the one or more user input components 1722 may include a touch-sensitive component (e.g., a touch-sensitive display or touchpad) that responds to the touch of a user input object (e.g., a finger or stylus). The touch-sensitive component may be useful for implementing a virtual keyboard. Other exemplary user input components include a computer mouse, a remote, a controller, a microphone, a traditional keyboard, or other means by which a user can provide user input. In some implementations, the one or more user input components 1722 may include one or more gesture processing engines for determining touch gestures, audio gestures, and / or physical gestures. The one or more user interfaces 1724 may be configured to acquire and / or display data. The one or more user interfaces 1724 may be associated with an operating system, one or more applications, one or more web platforms, and / or one or more devices. The one or more sensors 1726 may include one or more image sensors, one or more infrared sensors, one or more light detection and ranging (lidar) sensors, one or more audio sensors, one or more touch sensors, one or more sonic navigation and ranging (sonar) sensors, and / or one or more thermal sensors.

[0064] The data collection system 102 may be communicatively connected to the user computing system 104 via a network 1680, which may include the Internet (e.g., Ethernet and / or WiFi), Bluetooth, and / or direct wiring.

[0065] Network 1680 can include any type of communications network (e.g., a local area network (e.g., an intranet), a wide area network (e.g., the Internet), or some combination thereof) and can include any number of wired or wireless links. Communications over network 1680 can occur over any type of wired and / or wireless connections using a wide variety of communications protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or security schemes (e.g., VPN, Secure HTTP, SSL).

[0066] Computing system 100 may be utilized to implement the systems and methods disclosed herein. Other computing systems, including other system configurations, may be utilized to implement the systems and methods disclosed herein.

[0067] 9, the present disclosure is further directed to a method 300 for determining a predicted lifespan of a component 10. One or more steps of such a method may be performed, for example, by computing system 100 as described herein.

[0068] For example, method 300 may include preparing component 10, such as for imaging component 10, as shown in step 310. Such preparation may include, for example, surface preparation of component 10 (e.g., acid etching, electrochemical or other chemical treatment, or mechanical polishing, etc.) and / or other suitable processes that facilitate improved visibility of the grain structure of the surface of component 10.

[0069] The method 300 according to the present disclosure may further include, as shown in step 320, processing the image 200 of the component 10 to detect one or more grain structures 210 on the component 10 as described herein.

[0070] Method 300 according to the present disclosure may further include, as shown in step 330, comparing detected grain structure 210 to a stress map 220 of component 10, as described herein. The comparing step may include, for example, overlaying stress map 220 onto processed image 200 to compare the localization of detected grain structure 210 with stress directions 222 of stress map 220, as described herein.

[0071] Method 300 according to the present disclosure may further include, as shown in step 340, determining a predicted life of component 10 based on the detected localization of grain structure 210 and stress map 220, as described herein. Such determining step may include, for example, determining a creep probability based on the detected localization of grain structure 210 and stress directions 222 of stress map 220, and determining a predicted life of component 10 based on the creep probability, as described herein.

[0072] In an exemplary embodiment, the determining step is further based on one of the material type of the component, the age of the component 10, and / or the temperature at which the component 10 is used, as described herein.

[0073] Further aspects of the invention are provided by the subject matter of the following clauses.

[0074] A method for determining a predicted life of a component, comprising: processing, by a computing system having one or more processors, an image of the component to detect grain structure on the component. The method further comprises comparing, by the computing system, the detected grain structure to a stress map of the component. The method further comprises determining, by the computing system, a predicted life of the component based on the localization of the detected grain structure and the stress map.

[0075] 1. A computing system for determining a predicted life of a component, comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations including processing an image of the component to detect grain structure on the component, comparing the detected grain structure to a stress map of the component, and determining a predicted life of the component based on the localization of the detected grain structure and the stress map.

[0076] The system or method of any one or more embodiments disclosed herein, further comprising preparing the component, wherein preparing the component comprises surface preparation of the component.

[0077] The system or method of any one or more embodiments disclosed herein, wherein the image is a plurality of images.

[0078] The system or method of any one or more embodiments disclosed herein, wherein the image is one of a photograph, a video frame, a laser scanning image, an X-ray scanning image, a Laue orientation image, an electron channeling contrast image, or a three-dimensional scanned shape.

[0079] The system or method of any one or more embodiments disclosed herein, wherein processing the image includes performing a pixel-by-pixel analysis of the image.

[0080] The system or method of any one or more embodiments disclosed herein, wherein computer vision is utilized to perform pixel analysis of the image.

[0081] The system or method of any one or more embodiments disclosed herein, wherein the comparing step includes overlaying the stress map onto the processed image to compare the localization of the detected grain structure with the stress directions of the stress map.

[0082] The system or method of any one or more embodiments disclosed herein, wherein the determining step includes determining a creep probability based on the detected grain structure localization and stress directions of the stress map, and determining a predicted life of the component based on the creep probability.

[0083] The system or method of any one or more embodiments disclosed herein, wherein the determining step is further based on one of the component material, the component usage time, or the component usage temperature.

[0084] The system or method of any one or more embodiments disclosed herein, wherein the component is a directionally solidified component.

[0085] The system or method of any one or more embodiments disclosed herein, wherein the component is an equiaxed component.

[0086] The system or method of any one or more embodiments disclosed herein, wherein the component is a turbine component.

[0087] The technology described herein refers to servers, databases, software applications, and other computer-based systems, as well as actions taken and information transmitted to and from such systems. The inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functions among components. For example, the processes described herein may be implemented using a single device or component, or multiple devices or components working in combination. Databases and applications may be implemented on a single system or distributed across multiple systems. Distributed components may operate sequentially or in parallel.

[0088] This specification uses examples to disclose the invention, including the best mode, and to enable any person skilled in the art to practice the invention, including making and using any devices or systems, and performing any methods incorporated therein. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are considered to be within the scope of the claims if they contain structural elements that do not differ from the literal language of the claims, or if they contain equivalent structural elements that do not differ insubstantial way from the literal language of the claims. [Explanation of symbols]

[0089] 10. Components 11 Outer surface of component 10 50 X axis, axis 52 Y-axis, axis 54 Z axis, axis 100 Computing Systems 102 Data Collection System 104 User Computing System, Computing System 106 Imaging devices, devices 108 Three-dimensional data collection device, data collection device, device 110 Lens Assembly 112 Image Capture Device 120 Laser 124 sensors 130 Robot arm, 6 degrees of freedom arm 200 multiple images, one or more images, image, processed image 202 Cut-out 210 multiple detected particle structures, detected particle structures, particle structures, exemplary particle structures, one or more detected particle structures, one or more particle structures 220 Stress Map 222 Stress Direction 230 Arrow 232 Arrow 234 Arrow 236 Arrow 300 ways 310 steps 320 steps 330 steps 340 steps 1612 one or more processors 1614 memory 1616 Data 1618 command 1620 one or more machine learning models 1622 one or more user input components 1624 One or more user interfaces 1626 one or more sensors 1680 Network 1712 One or more processors 1714 memory 1716 Data 1718 command 1720 one or more machine learning models 1722 one or more user input components 1724 One or more user interfaces 1726 one or more sensors

Claims

1. A method (300) for determining a predicted life of a component (10), said method (300) comprising: processing (320) an image (200) of the component (10) by a computing system (100) comprising one or more processors (1612, 1712) to detect grain structures (210) on the component (10); comparing (330) the detected grain structure (210) with a stress map (220) of the component (10) by the computing system (100); determining (340) a predicted life of the component (10) based on the detected localization of the grain structure (210) and the stress map (220) by the computing system (100); The method (300).

2. The method (300) of claim 1, further comprising the step of preparing (310) the component (10), wherein the step of preparing (310) the component (10) comprises surface preparation of the component (10).

3. The method (300) of claim 1, wherein the image (200) is a plurality of images.

4. 10. The method of claim 1, wherein the image is one of a photograph, a video frame, a laser scanning image, an X-ray scanning image, a Laue orientation image, an electron channeling contrast image, or a three-dimensional scanned geometry.

5. The method (300) of claim 1, wherein processing (320) the image (200) comprises performing a pixel analysis of the image (200).

6. The method (300) of claim 5, wherein computer vision is utilized to perform the pixel analysis of the image (200).

7. 2. The method (300) of claim 1, wherein the comparing step (330) includes a step of overlaying the stress map (220) on the processed image (200) to compare the localization of the detected grain structure (210) with the stress direction (222) of the stress map (220).

8. 2. The method of claim 1, wherein the determining step includes determining a creep probability based on the localization of the detected grain structure and stress directions of the stress map, and determining a predicted life of the component based on the creep probability.

9. The method of claim 1, wherein the determining step is further based on one of a component material, an age of the component, or an operating temperature of the component.

10. The method (300) of claim 1, wherein the component (10) is a directionally solidified component.

11. The method (300) of claim 1, wherein the component (10) is an equiaxed component.

12. The method (300) of claim 1, wherein the component (10) is a turbine component.

13. A computing system (100) for determining a predicted life of a component (10), said system comprising: one or more processors (1612, 1712); one or more non-transitory computer-readable media that collectively store instructions (1618, 1718) that, when executed by the one or more processors (1612, 1712), cause the computing system (100) to perform operations; and the operation comprises: processing an image (200) of the component (10) to detect grain structures (210) on the component (10); comparing the detected grain structure (210) with a stress map (220) of the component (10); determining a predicted life of the component (10) based on the detected localization of the grain structure (210) and the stress map (220); A computing system (100) comprising:

14. The system of claim 13, wherein the image (200) is a plurality of images.

15. 14. The system of claim 13, wherein the image (200) is one of a photograph, a video frame, a laser scanning image, an X-ray scanning image, a Laue orientation image, an electron channeling contrast image, or a three-dimensional scanned geometry.

Images