Patent Description:
Turbomachinery components of gas turbine engines are subjected to extreme conditions during operation. Thus, such components deteriorate over time. Due to the high value of certain turbomachinery components (e.g., compressor and turbine blades), many times such damaged components are removed from the engine and repaired by a blade regeneration process. The blade regeneration process typically includes pre-treating the blade, depositing material onto the blade (e.g., the blade tip), recontouring the blade to desired specifications, and subjecting the blade to one or more post-treatment processes.

Recontouring turbomachinery components has conventionally been an iterative and time consuming process. In a typical recontouring process, a component is first inspected such that an operator can determine a machine offset, the component is then machined using the manually-determined machine offset, and the process iterates between inspection and machining until the component is recontoured to desired specifications. The recontouring process of engine blades has conventionally required manual intervention to determine the machine offset due to the significant part-to-part variation in the deterioration of the blades, the geometrical shape of the blades, the wear on the machining tool used to recontour the components (e.g., the wear on a belt of a grinder), and the uncertainties of the overall recontouring system. Accordingly, attempts at automating the recontouring process have been unsuccessful.

Therefore, improved systems and methods for recontouring components of gas turbine engines would be useful. More specifically, systems and methods for automating the recontouring process for components of gas turbine engines would be beneficial.

<CIT> describes a method of contouring a component by manipulating an engineering model with reference to a current configuration of the component.

<CIT> describes an inspection and sorting system for parts repair.

<CIT> describes an adaptive machining controller.

Exemplary aspects of the present disclosure are directed to methods and systems for recontouring components of gas turbine engines.

One exemplary aspect of the present disclosure is directed to a method for recontouring a component defining one or more sections, according to claim <NUM>.

Another exemplary aspect of the present disclosure is directed to a recontouring system for recontouring a component of a gas turbine engine, according to claim <NUM>.

Reference now will be made in detail to embodiments of the present disclosure, one or more example(s) of which are illustrated in the drawings. Each example is provided by way of explanation of the present disclosure, not limitation of the present disclosure.

Exemplary aspects of the present disclosure are directed to methods and systems for recontouring components of gas turbine engines. More particularly, exemplary aspects of the present disclosure are directed to systems and methods that include and/or leverage a cluster of machine-learned models, such as deep neural networks, to determine machine offsets for particular sections of a component. The machine offsets can then be used to adjust a material removal tool path of a material removal tool in real time. In this way, such components can be recontoured to desired specifications with high accuracy and without need for time consuming, iterative steps. Accordingly, the systems and methods of the present disclosure include features that reduce the component regeneration cycle time and produce recontoured components with high accuracy.

Further aspects and advantages of the present subject matter will be apparent to those of skill in the art. Exemplary aspects of the present disclosure will be discussed in further detail with reference to the drawings. The terms "upstream" and "downstream" refer to the relative flow direction with respect to fluid flow in a fluid pathway. For example, "upstream" refers to the flow direction from which the fluid flows, and "downstream" refers to the flow direction to which the fluid flows. "HP" denotes high pressure and "LP" denotes low pressure. Further, as used herein, the terms "axial" or "axially" refer to a dimension along a longitudinal axis of an engine. The term "forward" used in conjunction with "axial" or "axially" refers to a direction toward the engine inlet, or a component being relatively closer to the engine inlet as compared to another component. The term "rear" used in conjunction with "axial" or "axially" refers to a direction toward the engine nozzle, or a component being relatively closer to the engine nozzle as compared to another component. The terms "radial" or "radially" refer to a dimension extending between a center longitudinal axis (or centerline) of the engine and an outer engine circumference. Radially inward is toward the longitudinal axis and radially outward is away from the longitudinal axis. Moreover, the term "obtaining" means affirmatively or passively gaining, attaining, acquiring, collecting, or otherwise receiving the noted object, information, signals, data, transmission, etc..

Turning now to the drawings, <FIG> provides a cutaway perspective view of an exemplary engine according to an exemplary embodiment of the present disclosure. For this exemplary embodiment, the engine is configured as a gas turbine engine <NUM>. More particularly, the gas turbine engine <NUM> depicted in <FIG> is an aeronautical, high-bypass turbofan jet engine operatively configured to be mounted to or integral with an aircraft. The gas turbine engine <NUM> defines an axial direction A (extending parallel to or coaxial with a longitudinal centerline <NUM> provided for reference), a radial direction R, and a circumferential direction C extending about the axial direction A. The gas turbine engine <NUM> includes a fan section <NUM> and a core turbine engine <NUM> disposed downstream of the fan section <NUM>.

The exemplary core turbine engine <NUM> depicted includes a substantially tubular outer casing <NUM> that defines an annular inlet <NUM>. The outer casing <NUM> encases, in serial flow relationship, a compressor section <NUM> including a booster or LP compressor <NUM> and an HP compressor <NUM>; a combustion section <NUM>; a turbine section <NUM> including an HP turbine <NUM> and a LP turbine <NUM>; and a jet exhaust nozzle section (not depicted). An HP shaft or spool <NUM> drivingly connects the HP turbine <NUM> with the HP compressor <NUM>. A LP shaft or spool <NUM> drivingly connects the LP turbine <NUM> with the LP compressor <NUM>. The compressor section <NUM>, combustion section <NUM>, turbine section <NUM>, and jet exhaust nozzle section together define a core air flowpath <NUM> through the core turbine engine <NUM>.

The fan section <NUM> includes a fan <NUM> having a plurality of fan blades <NUM> coupled to a disk in a circumferentially spaced apart manner. As depicted, the fan blades <NUM> extend outwardly from the disk generally along the radial direction R. The fan blades <NUM> and the disk are together rotatable about the longitudinal centerline <NUM> by the LP shaft <NUM>.

Referring still to the exemplary embodiment of <FIG>, the disk is covered by rotatable spinner <NUM> aerodynamically contoured to promote an airflow through the plurality of fan blades <NUM>. Additionally, the exemplary fan section <NUM> includes an annular fan casing or outer nacelle <NUM> that circumferentially surrounds the fan <NUM> and/or at least a portion of the core turbine engine <NUM>. Moreover, the nacelle <NUM> is supported relative to the core turbine engine <NUM> by a plurality of circumferentially spaced outlet guide vanes <NUM>. Further, a downstream section <NUM> of the nacelle <NUM> extends over an outer portion of the core turbine engine <NUM> so as to define a bypass airflow passage <NUM> therebetween.

During operation of the gas turbine engine <NUM>, a volume of air <NUM> enters the gas turbine engine <NUM> through an associated inlet <NUM> of the nacelle <NUM> and/or fan section <NUM>. As the volume of air <NUM> passes across the fan blades <NUM>, a first portion of the air as indicated by arrows <NUM> is directed or routed into the bypass airflow passage <NUM> and a second portion of the air as indicated by arrow <NUM> is directed or routed into the LP compressor <NUM> of the core turbine engine <NUM>. The pressure of the second portion of air <NUM> is then increased as it passes across various stages of LP compressor stator vanes <NUM> (not shown extending annularly about the longitudinal centerline <NUM>) and LP compressor blades <NUM>. The air <NUM> then flows downstream to the HP compressor <NUM> where the air <NUM> is progressively compressed further by various stages of HP compressor stator vanes <NUM> (not shown extending annularly about the longitudinal centerline <NUM>) and HP compressor blades <NUM>. Thereafter, the compressed air is routed to the combustion section <NUM>.

The compressed second portion of air <NUM> discharged from the compressor section <NUM> mixes with fuel and is burned within the combustion section <NUM> to provide combustion gases <NUM>. The combustion gases <NUM> are routed from the combustion section <NUM> along the hot gas path <NUM>, through the HP turbine <NUM> where a portion of thermal and/or kinetic energy from the combustion gases <NUM> is extracted via sequential stages of HP turbine stator vanes <NUM> (not shown extending annularly about the longitudinal centerline <NUM>) that are coupled to the outer casing <NUM> and HP turbine rotor blades <NUM> that are coupled to the HP shaft or spool <NUM>, thus causing the HP shaft or spool <NUM> to rotate, thereby supporting operation of the HP compressor <NUM>. The combustion gases <NUM> are then routed through the LP turbine <NUM> where a second portion of thermal and kinetic energy is extracted from the combustion gases <NUM> via sequential stages of LP turbine stator vanes <NUM> (not shown extending annularly about the longitudinal centerline <NUM>) that are coupled to the outer casing <NUM> and LP turbine rotor blades <NUM> that are coupled to the LP shaft or spool <NUM>, thus causing the LP shaft or spool <NUM> to rotate, thereby supporting operation of the LP compressor <NUM> and/or rotation of the fan <NUM>.

The combustion gases <NUM> are subsequently routed through the jet exhaust nozzle section of the core turbine engine <NUM> to provide propulsive thrust. Simultaneously, the pressure of the first portion of air <NUM> is substantially increased as the first portion of air <NUM> is routed through the bypass airflow passage <NUM> before it is exhausted from a fan nozzle exhaust section <NUM> of the gas turbine engine <NUM>, also providing propulsive thrust. The HP turbine <NUM>, the LP turbine <NUM>, and the jet exhaust nozzle section at least partially define a hot gas path <NUM> for routing the combustion gases <NUM> through the core turbine engine <NUM>.

During operation of the gas turbine engine <NUM>, various turbomachinery components (e.g., compressor blades <NUM>, <NUM>, turbine blades <NUM>, <NUM>, compressor stator vanes <NUM>, <NUM>, turbine stator vanes <NUM>, <NUM>, fan blades <NUM>, etc.) are subjected to extreme pressures and temperatures, causing deterioration of the turbomachinery components over time. Moreover, the turbomachinery components can further be degraded or worn by foreign object debris (FOD). For example, the fan blades <NUM> are particularly vulnerable to FOD as the fan <NUM> is positioned at the inlet <NUM> of the gas turbine engine <NUM>. In some instances, damaged or deteriorated engine blades are removed from the gas turbine engine <NUM> and are subjected to a blade regeneration process.

<FIG> provides a flow chart of an exemplary blade regeneration process <NUM> according to an exemplary embodiment of the present disclosure. For this exemplary embodiment, the blade regeneration process <NUM> includes a pre-treatment process <NUM>, a material deposit process <NUM>, a recontouring process <NUM>, and a post-treatment process <NUM>. More specifically, after an engine blade is removed from an engine for servicing, the blade undergoes one or more pre-treatment processes <NUM> that sufficiently prepare the engine blade for the material deposit process <NUM>. For example, the worn or deteriorated portion of the engine blade can be removed. During the material deposit process <NUM>, material is cladded, deposited, or otherwise added to the existing blade. The excess material is then removed by a material removal tool during the recontouring process <NUM> such that the engine blade is reshaped to specification. Thereafter, the engine blade undergoes one or more post-treatment processes (e.g., one or more coatings can be applied to the blade). The present disclosure primarily concerns the recontouring process <NUM>.

With reference to <FIG> depicts an exemplary engine component <NUM> in a damaged condition, and more particularly, a damaged engine blade <NUM>. For instance, the damaged engine blade <NUM> can be a compressor or turbine blade. <FIG> provides the engine component <NUM> of <FIG> with the damaged portion removed. <FIG> provides the engine component <NUM> of <FIG> having a block of material deposited thereon.

As shown in <FIG>, the engine blade <NUM> has experienced significant deterioration, and more particularly, the engine blade <NUM> has experienced significant abrasion or wear along a blade tip <NUM> of the engine blade <NUM>. Engine blades can experience a wide variety of damage types or failures, including microstructural change, oxidation, cracks, abrasion, deformation, and entire breakages. Such deterioration negatively affects engine performance.

As shown in <FIG>, during the pre-treatment process <NUM> a deteriorated portion <NUM> (<FIG>) of the engine blade <NUM> is removed. Thereafter, as shown in <FIG>, material is cladded or otherwise added to the existing engine blade <NUM>. During the material deposit process <NUM>, in some exemplary embodiments, the deposited material <NUM> can be welded to the existing engine blade <NUM>. For the illustrated embodiment of <FIG>, the deposited material <NUM> is a cuboid-shaped metallic material welded to the engine blade <NUM>. The deposited material <NUM> can be any suitable material, including but not limited to a nickel-based super alloy, ceramic materials, ceramic matrix composite materials (CMCs), etc. Once the deposited material <NUM> has been welded to or otherwise added to the engine blade <NUM>, the engine blade <NUM> is recontoured in accordance with desired specifications.

<FIG> provides a schematic view of an exemplary recontouring system <NUM> operatively configured to recontour an engine component <NUM> according to an exemplary embodiment of the present disclosure. In this exemplary embodiment, the engine component <NUM> is a turbine blade of a gas turbine engine, such as one of the HP turbine blades <NUM> of the gas turbine engine of <FIG>. As depicted in <FIG>, after the material deposit process <NUM>, the engine component <NUM> having a block of material deposited thereon proceeds to the recontouring system <NUM> where the engine component <NUM> undergoes the recontouring process <NUM>. After being recontoured, the processed engine component <NUM> proceeds to the post-treatment process <NUM> as shown in <FIG>. For this exemplary embodiment, the recontouring system <NUM> includes an inspection device <NUM>, a display device <NUM>, a material removal tool <NUM>, a robotic manipulator <NUM>, and a computing system <NUM>. Each will be discussed in turn.

The inspection device <NUM> is operatively configured to scan an incoming engine component <NUM> such that features or parameters of the component can be resolved or extracted. For example, the inspection device <NUM> can sense the current condition of particular sections of the engine component <NUM> (e.g., the amount of deterioration of the component, the amount of material deposited thereon, etc.). More particularly, the inspection device <NUM> can sense the angular deviation of the engine component <NUM> with respect to a reference datum plane, the positioning of the block of material deposited on the existing engine blade, as well as other parameters that describe a current condition of the engine component <NUM> or a particular section of the component. Moreover, the inspection device <NUM> is operatively configured to sense or measure one or more parameters indication of the condition of the recontouring system <NUM>, such as e.g., how much the material removal tool <NUM> is worn or the overall uncertainties in the system.

The inspection device <NUM> can be any suitable device. For instance, the inspection device <NUM> can be a laser-based 2D or 3D scanner. As another example, the inspection device <NUM> can be an optical-tracking 3D scanning device that may capture the details, features, or parameters of the incoming engine component <NUM>. As another example, the inspection device <NUM> can be a 2D laser-based lines scanner configured to capture the profile parameters of each section of the component. Other suitable inspection devices are contemplated. In certain exemplary embodiments, the inspection device <NUM> can pivot about the incoming engine component <NUM> such that a scan of the component can more easily be obtained. For example, inspection device <NUM> can be operatively connected with a robotic arm of robotic manipulator <NUM> that is movable through <NUM> degrees of freedom. In this way, the inspection device <NUM> can capture the profile parameters of the component from various angles and perspectives.

The display device <NUM> is operatively configured to display information to a user regarding the operation and status of the recontouring system <NUM>. The display device <NUM> can include one or more user input devices for manipulating the recontouring system <NUM>. Such user input devices can include one or more of a variety of electrical, mechanical, or electro-mechanical input devices including rotary dials, push buttons, touch pads, and touch screens. In some exemplary embodiments, the display device <NUM> can include or represent a general purpose I/O ("GPIO") device or functional block. The display device <NUM> can be any suitable display device.

The material removal tool <NUM> is operatively configured to remove a portion of the deposited material <NUM> from the engine component <NUM> such that the engine component <NUM> can be shaped or recontoured to specification. The material removal tool <NUM> can be any suitable tool. For instance, for the illustrated embodiment of <FIG>, the material removal tool <NUM> is a belt grinder having an abrasive belt <NUM> operatively configured to grind or sand a portion of the deposited material <NUM> from the engine component <NUM>. In yet other exemplary embodiments, the material removal tool <NUM> can be a grinding wheel, an endmill, a polishing disc, a cutter or cutting tool, or any other suitable abrasive tool. In yet other exemplary embodiments, the material removal tool <NUM> can be a tool capable of removing material from a workpiece without the tool physically contacting the workpiece, such as e.g., electrical discharge machining, electrochemical grinding, electrochemical machining, etc. The material removal tool path of the material removal tool <NUM> can be adjusted in accordance with one or more machine offsets as will be described in greater detail herein.

The robotic manipulator <NUM> is operatively configured to hold the engine components <NUM> and cycle them through the recontouring system <NUM>. For example, the robotic manipulator <NUM> can be a robotic arm configured to hold or rotate the engine component <NUM> through six DOF. The robotic manipulator <NUM> can cycle the engine component <NUM> from the inspection device <NUM> to the material removal tool <NUM>. In some exemplary embodiments, the robotic manipulator <NUM> can adjust the orientation or position of the engine component <NUM> during machining such that the material removal tool path can be adjusted without adjusting the material removal tool <NUM>.

As further shown in <FIG>, the recontouring system <NUM> further includes computing system <NUM>. Computing system <NUM> is operatively configured to control various aspects of the recontouring system <NUM>, such as e.g., the robotic manipulator <NUM>, the inspection device <NUM>, the display device <NUM>, the material removal tool <NUM>, including the abrasive belt <NUM>. As shown by the dashed lines in <FIG>, the computing system <NUM>, and more particularly the devices of the computing system <NUM>, are communicatively coupled with the inspection device <NUM>, the display device <NUM>, the robotic manipulator <NUM>, and the material removal tool <NUM>. The devices of the computing system <NUM> can be communicatively coupled with the various components of the recontouring system <NUM> in any suitable manner, such as e.g., by one or more wired or wireless connections.

For this exemplary embodiment, the computing system <NUM> of the recontouring system <NUM> includes one or more computing device(s) <NUM>. The computing device(s) <NUM> can include one or more processor(s) <NUM> and one or more memory device(s) <NUM>. The one or more processor(s) <NUM> can include any suitable processing device, such as a microprocessor, microcontroller, integrated circuit, logic device, and/or other suitable processing device. The one or more memory device(s) <NUM> can include one or more computer-readable media, including, but not limited to, non-transitory computer-readable media, RAM, ROM, hard drives, flash drives, and/or other memory devices.

The one or more memory device(s) <NUM> can store information accessible by the one or more processor(s) <NUM>, including computer-readable instructions <NUM> that can be executed by the one or more processor(s) <NUM>. The instructions <NUM> can be any set of instructions that when executed by the one or more processor(s) <NUM>, cause the one or more processor(s) <NUM> to perform operations. In some embodiments, the instructions <NUM> can be executed by the one or more processor(s) <NUM> to cause the one or more processor(s) <NUM> to perform operations, such as any of the operations and functions for which the computing device(s) <NUM> are configured. The instructions <NUM> can be software written in any suitable programming language or can be implemented in hardware. Additionally, and/or alternatively, the instructions <NUM> can be executed in logically and/or virtually separate threads on processor(s) <NUM>.

The memory device(s) <NUM> can further store data <NUM> that can be accessed by the one or more processor(s) <NUM>. For example, the data <NUM> can include parameters or features descriptive of the condition of all of the incoming engine components <NUM>. For example, the parameters or features can be descriptive of the engine component's condition with respect to deformation, the amount, position, and orientation of the material added during the material deposit process <NUM>, but also to other factors like thickness of the abrasive belt <NUM> used for grinding and sanding and the amount of material that is removed from each engine component <NUM> during the recontouring process <NUM>, which is typically determined in a post-inspection process.

The computing device(s) <NUM> can also include a communication interface <NUM> used to communicate, for example, with the other components of the recontouring system <NUM> over other components over a network. The communication interface <NUM> can include any suitable components for interfacing with one or more network(s), including for example, transmitters, receivers, ports, controllers, antennas, and/or other suitable components. The network can be any type of communications network, such as a local area network (e.g., intranet), wide area network (e.g., Internet), or some combination thereof and can include any number of wired or wireless links. In general, communication interface <NUM> can communicate over networks via any type of wired and/or wireless connection, using a wide variety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g., VPN, secure HTTP, SSL).

As further shown in <FIG>, one or more computing devices <NUM> of the computing system <NUM> can store or otherwise include one or more machine-learned models <NUM> or a cluster of machine-learned models. For example, the models <NUM> can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other multi-layer non-linear models. In some exemplary embodiments, the machine-learned model <NUM> can be operatively configured to output a machine offset that can be used for recontouring the engine component <NUM>.

In some various embodiments, the machine-learned model <NUM> is a machine or statistical learning model structured as one of a linear discriminant analysis model, a partial least squares discriminant analysis model, a support vector machine model, a random tree model, a logistic regression model, a naive Bayes model, a K-nearest neighbor model, a quadratic discriminant analysis model, an anomaly detection model, a boosted and bagged decision tree model, an artificial neural network model, a C4. <NUM> model, a k-means model, or a combination of one or more of the foregoing.

Referring still to <FIG>, as shown, at least one of the computing devices <NUM> of computing system <NUM> includes a model trainer <NUM> that is operatively configured to train one or more of the machine-learned models <NUM> using various training or learning techniques. For example, where the machine learned-model <NUM> is a neural network, such training or learning techniques can include, for example, backwards propagation of errors. In some embodiments, performing backwards propagation of errors can include performing truncated backpropagation through time. The model trainer <NUM> can perform any number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models <NUM> being trained.

The model trainer <NUM> can train one or more of the models <NUM> based on a set of training data <NUM>. The training data <NUM> can include, for example, parameters or features indicative of a condition of a particular engine component after being removed from an engine and undergoing the material deposit process <NUM> as well as information about the material removal tool (e.g., the belt thickness at the time of material removal) and information about the recontouring system <NUM> overall. The training data <NUM> can further include the machine offsets used to recontour the engine component to desired specification. With known inputs (i.e., the condition of the incoming engine component, the belt thickness, and the behavior of the recontouring system <NUM>) and corresponding outputs (the machine offset used to machine the engine component to specifications), the model <NUM> can be trained.

Once the model trainer <NUM> has been trained the models <NUM> using the training data <NUM>, test or validation data <NUM> can be used to test or validate the models <NUM>. The test or validation data <NUM> can be made up by data indicative of parameters or features descriptive of a condition of a particular engine component after being removed from an engine and undergoing the material deposit process <NUM> as well as information about the material removal tool (e.g., the belt thickness at the time of material removal) and information about the recontouring system <NUM> overall. The validation data <NUM> includes new inputs and corresponding outputs that are used to validate the models <NUM>. Once the machine-learned models <NUM> are trained and validated, the models <NUM> can be used to output machine offsets such that the material removal tool path of the material removal tool <NUM> can be adjusted automatically. In this way, the engine components <NUM> cycling through the recontouring system <NUM> can be recontoured to specification.

<FIG> provides an exemplary recontouring process <NUM> according to an exemplary embodiment of the present disclosure. Generally, the exemplary recontouring process <NUM> includes an inspection process <NUM>, a material removal process <NUM>, and a post-inspection process <NUM>. As shown in <FIG>, various parameters P are extracted or otherwise obtained during the inspection process <NUM>. In particular, various parameters P descriptive or indicative of a condition of one of the sections of the engine component <NUM> are extracted or otherwise obtained, and in some embodiments, parameters indicative of a condition of the recontouring system <NUM> are extracted or otherwise obtained, such as e.g., a condition of the thickness of the abrasive belt <NUM>. After parameters P associated with each section of the engine component <NUM> and parameters P associated with the condition of the recontouring system <NUM> are obtained, the parameters P are input into a machined-learned model <NUM>, such as e.g., a cluster of deep neural networks. In particular, the parameters P associated with a particular section of the engine component <NUM> and the condition of the recontouring system <NUM> are grouped as subsets, and each subset is input into a corresponding machine-learned model. As an output of each of the machine-learned models, a section machine offset is received or otherwise obtained. Based at least in part on the machine offsets, a material removal tool path is adjusted such that the engine component <NUM> is recontoured in accordance with desired specifications. The engine component <NUM> then undergoes the post-inspection process <NUM> in which the engine component <NUM> is checked for accuracy. Based on the accuracy of the processed engine component <NUM>, the current behavior or condition of the recontouring system <NUM> can be determined. The material removal tool path can be adjusted for subsequent engine components cycled through the recontouring system <NUM> based at least in part on the behavior or condition of the recontouring system <NUM>.

As noted above, the parameters P obtained during the inspection process <NUM> can be descriptive or indicative of a condition of one of the sections of the engine component <NUM>. The engine component can be segmented into various sections. <FIG> provides the engine component <NUM> of <FIG> segmented into one or more sections S. For this exemplary embodiment, the engine component <NUM> is segmented into eight (<NUM>) sections. In alternative exemplary embodiments, the engine component <NUM> can be segmented into any suitable number of sections, such as e.g., ten (<NUM>) sections, one hundred sections (<NUM>), or one thousand (<NUM>,<NUM>) sections. As will be explained more fully below, by segmenting the engine component <NUM> into sections S, the part-to-part variation in deterioration, the complexity of the blade geometry, the uncertainty in the positioning, size, and orientation of the deposited material <NUM>, the material removal tool thickness or wear, and the uncertainty of the recontouring system can be broken down into a group of more manageable sub-problems. In this way, the trained machine-learned models can output more accurate machine offsets that can be used to adjust the material removal tool path of the material removal tool <NUM>.

Returning to <FIG>, each process of the exemplary recontouring process <NUM> will now be described in further detail. As noted above, during the inspection process <NUM>, various parameters P are extracted or otherwise obtained. For instance, the parameters P can be indicative of the engine component's condition with respect to deformation, the amount, position, and orientation of the deposited material <NUM> added during the material deposit process <NUM> (<FIG>). Additionally, other parameters P indicative of the condition of the recontouring system <NUM> can be received or otherwise obtained. For instance, the parameters P can include the thickness of the abrasive belt <NUM> used for grinding and sanding and the amount of material that is actually removed from each engine component <NUM> during the recontouring process <NUM>, which is typically determined in the post-inspection process <NUM>.

<FIG> provides a side, perspective view of engine component <NUM> depicting an exemplary technique for determining an angular deviation parameter ϕ according to an exemplary embodiment of the present disclosure. As noted above, one exemplary parameter that can be obtained during the inspection process <NUM> is the angular deviation ϕ of the engine component <NUM> with respect to a reference datum plane DP. As shown in <FIG>, the engine component <NUM> defines a direction V, a lateral direction L, and a transverse direction T. Each of the vertical direction V, lateral direction L, and transverse direction T are mutually perpendicular to one another and form an orthogonal direction system.

For this embodiment, the angular deviation ϕ represents the section bend with respect to the reference datum plane DP extending in a plane along the vertical and lateral directions V, L. More particularly, for this embodiment, the reference datum plane DP extends in a plane extending along the vertical and lateral directions V, L coplanar with a plane extending where a leading edge <NUM> of an airfoil <NUM> of the engine component <NUM> connects with a blade platform <NUM> of the engine component <NUM>. It will be appreciated that the reference datum plane DP may be a plane extending from other suitable locations along the transverse direction T.

Each section S of the engine component <NUM> can be altered in many different ways over its service life, which can greatly affect the recontouring result. In particular, the engine component <NUM> can be deformed or bent out of shape during operation of the gas turbine engine. Thus, the angular deviation ϕ is measured and forwarded to the machine-learned model <NUM> as an input parameter. In this way, the system can adapt or adjust the material removal tool path based at least in part on this parameter.

<FIG> provides a side, perspective view of exemplary engine component <NUM> depicting an exemplary technique for determining a step position parameter δ according to an exemplary embodiment of the present disclosure. One exemplary parameter that can be obtained during the inspection process <NUM> is the step position δ of the engine component <NUM>. As shown in <FIG>, for this embodiment, the step position δ is measured as a distance between a top <NUM> of the deposited material <NUM> to a bottom <NUM> of the deposited material <NUM> along the vertical direction V. The step position δ (i.e., the vertical length of the deposited material) may vary from section-to-section and from part-to-part. These variations can affect the recontouring result. The step position δ of the deposited material <NUM> may vary from part-to-part and section-to-section due to the impreciseness of the material deposit process <NUM> (e.g., a welding process). Thus, the step position δ of the deposited material <NUM> is measured and forwarded to the machine-learned model <NUM> as an input parameter. In this way, the system can adapt or adjust the material removal tool path based at least in part on this parameter.

<FIG> provides a side, perspective view of exemplary engine component <NUM> depicting an exemplary technique for determining a tool state parameter ρ according to an exemplary embodiment of the present disclosure. One exemplary parameter that can be obtained during the inspection process <NUM> is the tool state parameter ρ. The tool state ρ is a parameter indicative of a condition of the material removal tool <NUM>. For instance, where the material removal tool <NUM> is abrasive belt <NUM>, the condition of the material removal tool <NUM> can be descriptive of a thickness of the abrasive belt <NUM>.

For this exemplary embodiment, to determine the tool state ρ, the material removal tool <NUM> performs a blind pass on the deposited material <NUM>. Stated differently, the material removal tool <NUM> machines the engine component <NUM> with a degree of margin from the desired shape. The newly exposed surface of the engine component <NUM> can then be used as a reference for determining the characteristics or behavior of the abrasive belt <NUM>. Next, as shown in <FIG>, the tool state parameter ρ is measured along the engine component <NUM> for each section S as a distance between an edge <NUM> of the airfoil <NUM> of the engine component <NUM> to an edge <NUM> of the deposited material <NUM> along the transverse direction T. More particularly, for this embodiment, the tool state parameter ρ is measured along the engine component <NUM> for each section S as a distance between an edge <NUM> of the airfoil <NUM>, which in this embodiment is a trailing edge of the airfoil <NUM>, to an edge <NUM> of the deposited material <NUM>, which in this embodiment is the trailing edge of the deposited material, along the transverse direction T. Since this blind pass is performed with constant parameters, the resulting measurement varies as a function of the belt thickness. The tool state parameter ρ may vary from section-to-section and from part-to-part. These variations can affect the recontouring result. Thus, the tool state parameter ρ is measured during the inspection process <NUM> and forwarded to the machine-learned model <NUM> as an input parameter. In this way, the system can adapt or adjust the material removal tool path based at least in part on this parameter.

In alternative exemplary embodiments, the tool state parameter ρ can be determined by other suitable techniques. For instance, the tool state parameter ρ can be determined by one or more sensors of the recontouring system <NUM>. The one or more sensors of the recontouring system <NUM> can acquire 3D scans of the material removal tool <NUM>. For example, where the material removal tool <NUM> is an abrasive belt <NUM>, the thickness of the belt can be scanned such that the thickness of the belt is known. The 3D scans can then be sent to one or more computing devices <NUM> of the computing system <NUM> for processing. The parameters or characteristics of the material removal tool <NUM> can be forwarded to the machine-learned model <NUM> such that the system can account for the variation in the material removal tool <NUM> over time. As will be appreciated, continuing with the example above, the abrasive belt <NUM> will have a particular thickness when machining a first component cycled through the recontouring system <NUM> and will have a different thickness when machining the one hundredth component cycled through the recontouring system <NUM>. That is, the abrasive belt <NUM> will have a larger thickness when machining the first component than when machining the one hundredth component. Other material removal tools, such as e.g., a cutter, can also have tool wear over time as well. The tool state parameter ρ takes the tool wear of the material removal tool <NUM> into account such that engine components can be more accurately recontoured.

<FIG> provides a side, perspective view of processed engine component <NUM> depicting an exemplary technique for determining a measured step parameter ε according to an exemplary embodiment of the present disclosure. One exemplary parameter that can be obtained during the post-inspection process <NUM> (i.e., after the material removal process <NUM>) is the measured step parameter ε of the processed engine component <NUM>. The measured step parameter ε provides information regarding the condition of the recontouring system <NUM>. Stated differently, the measured step parameter ε relates the incoming condition of the engine component <NUM> (i.e., the angular deviation ϕ and the step position δ) and the tool state ρ with the actual material removed during the material removal process <NUM>. In this way, the measured step parameter ε accounts for uncertainties in the recontouring system <NUM> and can make adjustments to the material removal tool path for subsequent engine components cycled through the recontouring system <NUM> to account for these uncertainties.

As shown in <FIG>, for this exemplary embodiment, the measured step parameter ε is measured along the recontoured engine component <NUM> for each section S as a distance between an edge <NUM> of a non-regenerated portion <NUM> of the processed engine component <NUM> (i.e., the existing portion of the processed engine component <NUM>), to an edge <NUM> of the regenerated portion <NUM> (i.e., the deposited material <NUM> of the processed engine component <NUM>) along the transverse direction T. Notably, this measurement is taken during the post-inspection process <NUM>. As shown in <FIG>, this parameter P can be fed back into the machine-learned model <NUM> such that subsequent engine components can be more accurately recontoured to specification.

When continuity between the regenerated portion <NUM> and the non-regenerated portion <NUM> has been achieved (i.e., a "flush condition" has been achieved), the system will recognize that the material removal tool path was set or adjusted accurately based on the machine offsets determined by the other parameters. In instances where the measured step ε is significant, the system will make adjustments accordingly. In this way, the measured step parameter ε acts as a "catch all" the uncertainties of the recontouring system <NUM>. The measured step parameter ε drives down the error for subsequent engine components <NUM> cycled through the recontouring system <NUM>.

Other exemplary parameters can be measured and forwarded to the machine-learned model as an input parameter in addition to the above named parameters. For example, in some embodiments, one exemplary parameter that can be measured is the twist angle or angle of twist of the engine component. The twist angle for engine or fan blades may be particular useful due to their complex geometric shapes. As another example, in some embodiments, one exemplary parameter that can be measured is the chord length of an engine or fan blade. As yet another example, in some embodiments, one exemplary parameter that can be obtained is the serial or batch number of the engine components. By obtaining the serial or batch number of the engine component, the material removal tool path can ultimately be adjusted in accordance with the notion that similar parts manufactured within the same batch or at the same manufacturing facility are more likely to have constructed in a similar manner. Moreover, as yet another example, in some embodiments, other exemplary parameters that can be obtained is the stage in which the engine blade was positioned on the engine, the service life of the engine blade (i.e., the number of hours in operation), the service life of the engine blade since the last maintenance overall, the length of the blade along the vertical direction, the standard operating pressure ratio of the engine from which the blade was removed, other dimensions of the engine component, the type of material of the blade, etc. Other exemplary parameters are possible.

<FIG> provides a flow diagram for determining one or more machine offsets according to an exemplary embodiment of the present disclosure. After the various parameters P are measured or sensed during the inspection process <NUM> (or post-inspection process <NUM>), one or more computing devices <NUM> of the computing system <NUM> receives, collects, or otherwise obtains a data set <NUM> made up of one or more subsets <NUM>. As shown in <FIG>, each section S<NUM>, S<NUM>,. SN of the engine component <NUM> has a corresponding subset <NUM> (i.e., subset S<NUM>, subset S<NUM>,. subset SN) that includes parameters P that are indicative of the condition that section S of the engine component <NUM> as well as parameters P indicative of the condition of the recontouring system <NUM> at the time of the material removal process <NUM>.

For instance, for this embodiment, subset S<NUM> includes as parameters the angular deviation ϕ and the step position δ of the deposited material <NUM> that describe or indicate the condition of section S<NUM> of the engine component <NUM>. Moreover, subset S<NUM> includes the tool state ρ of the abrasive belt <NUM> and the measured step ε that is indicative of the uncertainty of the recontouring system <NUM>. The measured step ε can be a parameter measured in the post-inspection process <NUM> of the previous processed component, for example. Further, subset S<NUM> can include other parameters P1, such as e.g., twist angle, total blade length, batch number, etc. Likewise, for this embodiment, subset S<NUM> includes as parameters the angular deviation ϕ and the step position δ that are indicative of the condition of section S<NUM> of the engine component <NUM>. Subset S<NUM> also includes the tool state ρ of the abrasive belt <NUM> and the measured step ε. Additionally, subset S<NUM> can include other parameters P2. Each section S of the engine component <NUM> can have a corresponding subset <NUM>. For instance, section SN has a corresponding subset SN.

The data set <NUM> is input into a machine-learned model <NUM>, which for this embodiment is a cluster of Deep Neural Networks (DNNs) <NUM>. As shown in <FIG>, each subset <NUM> is input into a corresponding DNN. For instance, subset S<NUM> of data set <NUM> is input into S<NUM> DNN, subset S<NUM> is input into S<NUM> DNN, and so on and so forth for each section S of the engine component <NUM>. For instance, as shown in <FIG>, subset SN is input into SN DNN. The machined-learned model <NUM> can include any suitable number of DNNs <NUM> such that each subset <NUM> of the data set <NUM> has a corresponding DNN.

As further shown in <FIG>, for this embodiment, each DNN contains an input layer, hidden layers, and an output layer. The input layer <NUM> of each DNN can include any suitable number of nodes or neurons. In particular, depending on the number of parameters of each subset <NUM>, the input layer <NUM> can contain a corresponding number of neurons. For instance, one neuron of the input layer <NUM> can be for the angular deviation parameter ϕ, another neuron of the input layer <NUM> can be for the step position parameter δ, another neuron of the input layer <NUM> can be for the tool state parameter ρ, another neuron of the input layer <NUM> can be for the measured step parameter ε, and yet another neuron of the input layer <NUM> can be for parameter P1 that can be indicative of a condition of one of the sections S of the engine component <NUM>. For instance, the parameter P1 can be the twist angle of the engine component <NUM>.

As the inputs are fed forward through their respective DNNs, a set of first weights W<NUM>, each of which may be different for each synaptic connection, are applied to the input values. Then, each neuron of the first hidden layer <NUM> adds the outputs from its corresponding synapses between the input layer <NUM> and the first hidden layer <NUM> and applies an activation function. Thereafter, the values from the activation function are fed forward to the second hidden layer <NUM> where a set of second weights W<NUM>, each of which may be different for each synaptic connection, is applied to the outputs of the activation functions of the first hidden layer <NUM>. Each neuron of the second hidden layer <NUM> adds the outputs from its corresponding synapses between the first hidden layer <NUM> and the second hidden layer <NUM> and applies an activation function. Thereafter, the values from the activation function of the second hidden layer <NUM> are fed forward to the output layer <NUM> where a set of third weights W<NUM>, each of which may be different for each synaptic connection, is applied to the outputs of the activation functions of the second hidden layer <NUM>. In alternative exemplary embodiments, the values from the second hidden layer <NUM> are forwarded to a further hidden layer or layers before reaching the output layer <NUM>. Each DNN can include any suitable number of hidden layers. The neuron of the output layer <NUM> receives the values from the synaptic connections and likewise applies an activation function to render an output of the network. In this example, the output of each DNN is a machine offset for a particular section S of the engine component <NUM>. As shown particularly in <FIG>, the output of the S<NUM> DNN is a machine offset S<NUM>, the output of the S<NUM> DNN is a machine offset S<NUM>, and the output of the SN DNN is a machine offset SN.

Notably, due to the architecture of the machine-learned model <NUM>, which in this embodiment is a cluster of DNNs <NUM> configured for parallel processing, the subsets <NUM> can be processed concurrently such that the machine offsets associated with each section S can be predicted in real-time to ultimately render an optimized material removal tool path. The subsets can be processed on multiple computing devices or GPUs or on a single processing unit, for example. Stated differently, the recontouring system <NUM> need not sequentially perform operations using an iterative, closed loop process between the material removal process and the inspection process to find the correct machine offset for each section S. Rather, for this exemplary embodiment, the recontouring system <NUM> performs a single computational step to obtain machine offsets for each section S of the engine component <NUM>. In this way, the blade regeneration cycle time, and more particularly the cycle time of the recontouring process <NUM>, can be completed more efficiently.

<FIG> provides a flow diagram for determining a material removal tool path according to an exemplary embodiment of the present disclosure. As shown, as an output of each DNN <NUM> (<FIG>), each section S of the engine component <NUM> has an associated machine offset. For instance, for this embodiment, section S<NUM> of the engine component <NUM> has an associated machine offset S<NUM>, section S<NUM> of the engine component <NUM> has an associated machine offset S<NUM>, and so on such that section SN has an associated machine offset SN. Each machine offset, for example, accounts for the part-to-part and section-to-section variation of each engine component <NUM>, as well as the uncertainty of the recontouring system <NUM>. Based on the machine offsets output from their respective DNNs <NUM>, the material removal tool path <NUM> can be adjusted or set and the material removal tool <NUM> (<FIG>) can machine or recontour the engine component <NUM> using the material removal tool path <NUM>. In this way, the engine component <NUM> can be recontoured in accordance with specifications. The result of the recontouring process <NUM> is processed engine component <NUM>. By using a cluster of deep neural networks <NUM> running in parallel, the machine offsets of each section S can be predicted in real-time such that the material removal tool path <NUM> can be adjusted to handle the part-to-part and section-to-section variation of the engine components <NUM> with accurate results. For instance, in some exemplary embodiments, accuracy up to <NUM> inches (<NUM>) can be achieved. Furthermore, the automation of determining the machine offsets reduces the blade regeneration cycle time.

<FIG> provides a flow diagram of an exemplary method (<NUM>) according to exemplary embodiments of the present disclosure. Some or all of the method (<NUM>) can be implemented by the recontouring system <NUM> described herein.

At (<NUM>), exemplary method (<NUM>) includes obtaining, by one or more computing devices, a data set comprised of one or more subsets each comprised of one or more parameters indicative of a condition of one of the sections of the component. For instance, one of the computing devices <NUM> of computing system <NUM> can obtain data set <NUM>. Data set <NUM> can include one or more subsets <NUM>. In some implementations, the data set <NUM> includes at least two subsets <NUM>. Each subset <NUM> can include one or more parameters indicative of the condition of a component, such as engine component <NUM>. In particular, exemplary parameters can include an angular deviation parameter ϕ, a step position parameter δ, a twist angle, a blade length, a batch or serial number of the component, etc..

At (<NUM>), exemplary method (<NUM>) includes inputting, by the one or more computing devices, the data set into a cluster of machine-learned models, each subset being input into a respective machine-learned model. For example, in some implementations, the cluster of machined-learned models can be a cluster of neural networks. In some implementations, the cluster of machined-learned models can be a cluster of deep neural networks. Each subset <NUM> of the data set <NUM> can be input into a respective machine-learned model of the cluster of models <NUM>.

At (<NUM>), exemplary method (<NUM>) includes determining, by the one or more computing devices, a machine offset for each section of the component based at least in part on the one or more parameters indicative of the condition of the section. For instance, one or more of the computing devices <NUM> of the computing system <NUM> can determine the machine offset for each section of the component. As the parameters of each subset <NUM> are processed by their respective models, the trained models determine the machine offsets based on the incoming parameter values.

At (<NUM>), exemplary method (<NUM>) includes obtaining, by the one or more computing devices, the machine offset for each section of the component as outputs of their respective machine-learned models. For example, one or more of the computing devices <NUM> of the computing system <NUM> can obtain a machine offset from each of the machine-learned models of the cluster. Each machine-learned model of the cluster of machine-learned models <NUM> is trained to output a particular machine offset based on the parameters obtained, as noted above.

At (<NUM>), exemplary method (<NUM>) includes adjusting, by the one or more computing devices, a material removal tool path based at least in part on the machine offsets. For instance, one or more of the computing devices <NUM> of the computing system <NUM> can process the obtained machine offsets and can determine an optimized material removal tool path. In some implementations, the machine offsets can each be output as values, and based on these values, the material removal tool path can be set or adjusted. In some implementations, the machine offsets can each be output as vectors, and based on these vectors, the material removal tool path can be adjusted in magnitude and direction. In some implementations, the outputs of the respective models can be a combination of values and vectors.

At (<NUM>), exemplary method (<NUM>) includes machining the component utilizing the material removal tool path. For instance, the material removal tool can include abrasive belt. Abrasive belt, utilizing the set or adjusted material removal tool path, can machine or recontour the engine component in accordance with desired specifications.

In some implementations, the component defines a reference datum plane, and wherein the one or more parameters indicative of the condition of one of the one or more sections of the component includes an angular deviation of the component with respect to the reference datum plane.

In some implementations, the engine component defines a vertical direction. In such implementations, the method (<NUM>) further includes adding a deposited material to the component prior to obtaining the data set, and wherein the deposited material extends along the vertical direction between a top and a bottom. Moreover, in such implementations, the condition of the component includes a step position indicative of a distance between the top and the bottom of the deposited material.

In some implementations, the recontouring system is used to recontour the component. In such implementations, each subset further includes one or more parameters indicative of a condition of the recontouring system. In such implementations, during determining, the machine offset for each section of the component is based at least in part on the one or more parameters indicative of the condition of the recontouring system.

In yet some implementations, the recontouring system includes a material removal tool having an abrasive belt. In such implementations, the condition of the recontouring system includes a tool state δ indicative of a condition of the material removal tool <NUM>.

In yet some implementations, the method (<NUM>) further includes adding a deposited material to the component prior to obtaining the data set. The method also includes machining the component utilizing the material removal tool path after adjusting the material removal tool path based at least in part on the machine offsets, and wherein after machining, the component defines a regenerated portion and a non-regenerated portion. Moreover, the method further includes performing a post-inspection process on the component. In such implementations, the one or more parameters indicative of the condition of the recontouring system includes a measured step ε, wherein the measured step ε is measured along the component for each of the sections of the component as a distance between an edge of the non-regenerated portion to an edge of the regenerated portion.

In yet some implementations, after inputting, the method (<NUM>) further includes processing, by the one or more computing devices, each subset in its respective machine-learned model in parallel.

<FIG> provides a flow diagram of another exemplary method (<NUM>) according to exemplary embodiments of the present disclosure. Some or all of the method (<NUM>) can be implemented by the recontouring system <NUM> described herein.

At (<NUM>), exemplary method (<NUM>) includes obtaining, by one or more computing devices, a data set comprised of a plurality of subsets, each subset comprised of one or more parameters indicative of a condition of one of the sections of the component and a condition of the recontouring system. For instance, one of the computing devices <NUM> of computing system <NUM> can obtain data set <NUM>. Data set <NUM> can include any suitable number of subsets <NUM>. Each subset <NUM> can include one or more parameters indicative of the condition of the component, such as engine component <NUM>. In particular, exemplary parameters can include an angular deviation parameter ϕ, a step position parameter δ, a twist angle, a blade length, a batch or serial number of the component, etc. In addition, exemplary parameters indicative of a condition of the recontouring system can include a tool state δ indicative of a condition of the material removal tool <NUM> and a measured step ε indicative of the condition or uncertainty of the regeneration system.

At (<NUM>), exemplary method (<NUM>) includes inputting, by the one or more computing devices, the data set into a machine-learned model comprised of a plurality of neural networks, each subset being input into a respective neural network. For instance, each subset <NUM> of the data set <NUM> can be input into a respective DNN of the machine-learned model. In some implementations, the neural networks are deep neural networks that include at least two hidden layers.

At (<NUM>), exemplary method (<NUM>) includes determining, by the one or more computing devices, a machine offset for each section of the component based at least in part on the one or more parameters indicative of the condition of the section and the condition of the recontouring system. In such implementations, as the inputs (i.e., parameters) are fed forward through their respective networks, the weights and activation functions are applied to the value of the parameters, as a result, a machine offset is output from each of the neural networks.

At (<NUM>), exemplary method (<NUM>) includes obtaining, by the one or more computing devices, the machine offset for each section of the component as outputs of their respective neural networks. For example, one or more of the computing devices <NUM> of the computing system <NUM> can obtain a machine offset from each of the neural networks <NUM>. Each neural network is trained to output a particular machine offset based on the parameters obtained.

In some implementations, the method (<NUM>) further includes machining the component utilizing the material removal tool path.

In some implementations, the method (<NUM>) further includes adding a deposited material to the component prior to obtaining the data set. Moreover, the method further includes machining the component utilizing the material removal tool path after adjusting the material removal tool path based at least in part on the machine offsets, and wherein after machining, the component defines a regenerated portion and a non-regenerated portion. In addition, the method further includes performing a post-inspection process on the component. In such implementations, the one or more parameters indicative of the condition of the recontouring system includes a measured step, wherein the measured step is measured along the component for each of the sections of the component as a distance between an edge of the non-regenerated portion to an edge of the regenerated portion.

In some implementations, during determining the machine offset for each section of the component, the machine offsets for each section are determined in parallel. Stated differently, the parameters of each subset are processed in parallel by their respective neural networks. In some implementations, the neural networks are deep neural networks. Moreover, in some implementations, the component is at least one of an engine blade and a fan blade of the gas turbine engine.

In some implementations, the component defines a reference datum plane and a vertical direction. In addition, the recontouring system includes a material removal tool having an abrasive belt. In such implementations, the method further includes adding a deposited material to the component prior to obtaining the data set. In such implementations, the deposited material extends between top and bottom along the vertical direction. The method also includes machining the component utilizing the material removal tool path after adjusting the material removal tool path based at least in part on the machine offsets, and wherein after machining, the component defines a regenerated portion and a non-regenerated portion. The method further includes performing a post-inspection process on the component. In such implementations, the one or more parameters indicative of the condition of one of the sections of the component include: an angular deviation of the component with respect to the reference datum plane; and a step position indicative of a distance between the top and the bottom of the deposited material. Moreover, in such implementations, the one or more parameters indicative of the condition of the recontouring system include: a tool state indicative of the condition of the material removal tool (e.g., the amount of tool wear); and a measured step measured along the component for each of the sections of the component as a distance between an edge of the non-regenerated portion to an edge of the regenerated portion.

In yet further implementations, one of the parameters indicative of the condition of one of the sections of the component is a twist angle. In some implementations, the component defines a vertical direction. In such implementations, one of the parameters indicative of the condition of one of the sections of the component is a length of the component along the vertical direction.

Although the present disclosure describes the recontouring process in the context of machining or recontouring an engine component or engine blade, it will be appreciated that the teachings and inventive concepts described herein can be applied to any suitable component.

The technology discussed herein makes reference to computing devices, databases, software applications, and other computer-based systems, as well as actions taken and information sent to and from such systems. For instance, computer-implemented processes discussed herein can be implemented using a single computing device or multiple computing devices working in combination. Such configurations can be implemented without deviating from the scope of the present disclosure.

This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. 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 intended to be within the scope of the claims if they include structural elements that do not differ from the literal language of the claims or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.

Claim 1:
A method (<NUM>) for recontouring a component (<NUM>) of a gas turbine engine (<NUM>), the component (<NUM>) defining a section (S), the method (<NUM>) comprising:
obtaining, by one or more computing devices (<NUM>), a data set (<NUM>) comprised of a plurality of subsets (<NUM>) each comprised of one or more parameters (P) indicative of a condition of the section (S) of the component (<NUM>);
inputting, by the one or more computing devices (<NUM>), the data set (<NUM>) into a cluster of machine-learned models (<NUM>), each subset (<NUM>) being input into a respective machine-learned model (<NUM>);
determining, by the one or more computing devices (<NUM>), a machine offset (SOFFSET) for the section (S) of the component (<NUM>) based at least in part on the one or more parameters (P) indicative of the condition of the section (S);
obtaining, by the one or more computing devices (<NUM>), the machine offset (SOFFSET) for the section (S) of the component (<NUM>) as outputs of their respective machine-learned models (<NUM>);
adjusting, by the one or more computing devices (<NUM>), a material removal tool (<NUM>) path (<NUM>) based at least in part on the machine offsets (SOFFSET); and
machining the component (<NUM>) utilizing the material removal tool (<NUM>) path (<NUM>); wherein:
a recontouring system (<NUM>) is used to recontour the component (<NUM>);
each subset (<NUM>) is further comprised of one or more parameters (P) indicative of a condition of the recontouring system (<NUM>);
during determining, the machine offset (SOFFSET) for the section (S) of the component (<NUM>) is based at least in part on the one or more parameters (P) indicative of the condition of the recontouring system (<NUM>);
the one or more parameters (P) indicative of a condition of the recontouring system (<NUM>) include a measured step (ε); and
the measured step (ε) is measured during a post-inspection process of a first component defining one or more first sections, the first component being machined prior to the component by the recontouring system and defining a regenerated portion and a non-regenerated portion after being machined, the measured step being measured along the first component for each of the first sections as a distance between an edge of the non-regenerated portion to an edge of the regenerated portion.