Method and system of manufacturing film cooling apertures by laser drilling

Abstract

A system (10) and method (100) for manufacturing a component (14) with optimum geometry includes determining a target mass flow rate (102) through an aperture (48) of the component (14) and determining geometric variances (104) associated with the aperture (48). Optimum geometry is selected based on the geometric variances and the target mass flow rate. Input variables for a manufacturing tool are determined that correspond to the optimum geometry and the component (14) is manufactured based on the selected input variables.

Description

TECHNICAL FIELD

[0001] The invention relates to manufacturing components, and more specifically to manufacturing a component to achieve an optimum mass air flow through an aperture.BACKGROUND

[0002] Gas turbine engines and other heat-producing machines often require cooling to maintain material temperatures within acceptable ranges. Thermal liners, heat shields, combustion chambers, and other components exposed to high temperature may utilize effusion cooling apertures to provide a cooling fluid flow along a surface of the component. While traditional manufacturing techniques are considered satisfactory for the intended purpose, variances of the aperture geometry, the component geometry, and the manufacturing tool contribute to reduced and / or excessive mass flow through the aperture. Since component and / or machine performance can be impacted by cooling mass flow variances, improvements to component manufacture are desirable.SUMMARY

[0003] A method for manufacturing a component with an aperture extending from an interior cavity through an exterior wall of the component, according to an example embodiment of this disclosure, includes determining a target mass flow rate through aperture. The method further includes determining a plurality of target geometric variances of the aperture, each geometric variance associated with a plurality of geometric dimensions of the aperture. The method further includes identifying an optimum geometry of the aperture based on the plurality of geometric variances and the target mass flow rate. The method further includes determining a plurality of input variables to a manufacturing tool corresponding to the optimum geometry and manufacturing the component using the manufacturing tool based on the plurality of input variables.

[0004] A system for manufacturing a component with an aperture extending from an interior cavity through an exterior wall of the component, according to another example embodiment of this disclosure, includes a manufacturing tool and a computing device. The manufacturing tool includes a plurality of input variables. The computing device includes a processor and memory encoded with instructions that, when executed by the processor, cause the system to determine a target mass flow rate through the aperture and determine a plurality of target geometric variances of the aperture, each geometric variance associated with a geometric dimension of the aperture. The instructions further cause the system to identify an optimum geometry of the aperture based on the plurality of target geometric variances and the target mass flow rate. The instructions further cause the system to determine the plurality of input variables to the manufacturing tool corresponding to the optimum geometry and manufacture the component based on the plurality of input variables using the manufacturing tool.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] FIG. 1 is a schematic view of a system for manufacturing a component with optimal aperture geometry. FIG. 2 is a partial isometric view of the component depicting multiple apertures. FIG. 3 is a cross-sectional view through one of the apertures of the component FIG. 4 is a flow chart describing a method for manufacturing a component with optimal aperture geometry. DETAILED DESCRIPTION

[0006] As disclosed herein are a system and a method for manufacturing a component with optimized aperture geometry. Features of the system and the method determine optimized aperture geometry such that the aperture outputs a target mass flow of cooling fluid based on as-built geometry for given cooling fluid properties and differential pressure across the aperture. Optimal aperture geometry maximizes cooling effect, which contributes to optimal engine performance. The system and method further reduce manufacturing variances by determining optimum input variables of the manufacturing tool used to produce the aperture with optimum geometry. By optimizing aperture geometry and the manufacturing input variables based on as-built component geometry, the error between the actual mass flow rate and the target mass flow rate delivered by the aperture are reduced or eliminated and, thereby, improve performance of effusion-cooled components.

[0007] FIG. 1 is a schematic view depicting system 10 for manufacturing component 14 with optimized aperture geometry. System 10 includes computing device 12 and manufacturing tool 16 that are used to manufacture component 14. Computing device 12 is an electronic device that communicates with manufacturing tool 16 via direct connection 18 or via network 20. Computing device 12 can be a computer, server, tablet, a smartphone, or other mobile computing device or stationary computing device, among other options. While the following disclosure refers to a computing device (singular), the method or features attributed to a single computing device can be distributed among multiple computing devices 12 in other examples of system 10. That is, functionality attributed herein to computing device 12 can, in certain examples, be distributed among multiple computing devices 12 that electrically communicate with each other and / or with manufacturing tool 16 via direct connection 18 and / or network 20. Computing device 12 includes processor 22, memory 24, user interface 26, and communication device 28.

[0008] Processor 22 can execute software, applications, and / or programs stored on memory 24. Examples of processor 22 can include one or more of a processor, a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry. Processor 22 can be entirely or partially mounted on one or more circuit boards.

[0009] Memory 24 is configured to store information and, in some examples, can be described as a computer-readable storage medium. Memory 24, in some examples, can be described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term "non-transitory" can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, memory 24 is a temporary memory. As used herein, a temporary memory refers to a memory having a primary purpose that is not long-term storage. Memory 24, in some examples, is described as volatile memory. As used herein, a volatile memory refers to a memory that does not maintain stored contents when power to the memory is turned off. Examples of volatile memories can include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories. In some examples, memory 24 is used to store program instructions for execution by processor 22. Memory 24, in one example, is used by software, applications, and / or programs running on computing device 12 to temporarily store information during program execution.

[0010] Memory 24, in some examples, also includes one or more computer-readable storage media. Memory 24 can be configured to store larger amounts of information than volatile memory. Memory 24 can further be configured for long-term storage of information. In some examples, memory 24 includes non-volatile storage elements. Examples of such non-volatile storage elements can include, for example, magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

[0011] Memory 24 can store instructions that, when executed by processor 22, cause computing device 12 to perform one or more methods and / or other functions described herein. Instructions can include software, applications, routines, algorithms, and / or other code stored on memory 24.

[0012] User interface 26 is an input and / or output device that enables a user to control the operation of computing device 12 and thereby control the manufacture of component 14. User interface 26 can include one or more of a sound card, a video graphics card, a speaker, a display device (such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, etc.), a touchscreen, a keyboard, a mouse, a joystick, or other type of device for facilitating input and / or output of information in a form understandable to users, machines, or other computing devices.

[0013] Communication device 28 is an input and / or output device that enables computing device 12 to electrically communicate with manufacturing tool 16, for example, with control module 30 of manufacturing tool 16. Communication device 28 can include a network interface card (NIC), a modem, a bridge, a hub, and / or a router, which may communicate with other network-attached components via wired and / or wireless connections.

[0014] Network 20 can include one or more local area networks (LAN) and / or one or more wide area networks (WAN) that define one or more electrical communication paths between computing device 12 and manufacturing tool 16. Local area networks and / or wide area networks can include wired communication and / or wireless communication between and among devices on network 20. Examples of wired communication include ethernet cable connections, telephone cable connections, coaxial cable connections, optical cable connections, and the like. Examples of wireless communication include Wi-Fi signals, cellular signals, infrared signals, radios signals and the like. Network 20 enables computing device 12 to be collocated with manufacturing tool 16 or located remotely with respect to each other.

[0015] Component 14 is any part or assembly of parts that includes at least one aperture and, in some examples, includes multiple apertures that can be arranged in arrays and / or groups. Examples of component 14 include combustion chambers, heat shield, liners, baffle plates, and nozzles, among other potential applications.

[0016] Manufacturing tool 16 is any machine tool capable of producing one or more apertures within component 14. Examples of manufacturing tools suitable for forming an aperture through a wall of component include a laser drilling machine, a conventional drilling machine, an electrical discharge machine (EDM), and an electro-chemical discharge machine, among other possible manufacturing tools. Each example of manufacturing tool 16 includes control module 30 and one or more input variables 32 that vary the operation of manufacturing tool 16 and thereby vary the aperture geometry of component 14.

[0017] As depicted, manufacturing tool 16 can be a laser drilling machine that can include, for example, base structure 34, mount 36, emitter 38, support arm 40, control module 30, and supply 42. Base structure 34 affixes manufacturing tool 16 to the ground or floor and prevents relative motion therewith. Mount 36 mechanically connects with base structure 34 to support component 14 and / or fixture 44. In some examples, mount 36 can be displaced and / or rotated relative to base structure 34 to position component 14 and / or fixture 44 relative to emitter 38. Component 14 can be attached to or otherwise restrained by mount 36 directly or can be attached to mount 36 indirectly via fixture 44. Emitter 38 produces a coherent beam of electromagnetic radiation (e.g., a laser) for melting and removing material from component 14 to form an aperture. Emitter 38 is attached to carrier 46, which combined with support arm 40, translates emitter 38 relative to mount 36 to position emitter 38 at a three-dimensional position and angular orientation. Emitter 38 can receive electrical power from supply 42 for stimulating electromagnetic radiation emission from emitter 38. Control module 30 electrically communicates with one or more actuators (not shown), supply 42, and emitter 38 to position emitter 38 relative to component 14 and discharge emitter 38 to form one or more apertures. Example input variables 32 for laser drilling machine (e.g., manufacturing tool 16) can include a three-dimensional position of emitter 38, a three-dimensional angular orientation of emitter 38, emitter energy density, emitter focal distance, number of shots or shutter count, discharge duration, and spacing between adjacent apertures, among other potential input variables.

[0018] FIG. 2 is an isometric view of component 14 depicting aperture 48 extending through exterior wall 50. Exterior wall 50 of component 14 is bound by interior surface 50A and exterior surface 50B, which is spaced from interior surface 50A to define wall thickness T1. Exterior surface 50B can be formed by coating 52 of thickness T2 applied to exterior wall 50, in certain examples. For example, exterior surface 50B of component can be formed by a thermal barrier coating or an environmental coating, among other potential coatings. Aperture 48 extends through exterior wall 50 and coating 52, if present, from interior surface 50A to exterior surface 50B. Interior surface 50A bounds interior cavity 54 which contains cooling fluid at static pressure P1 and temperature T1 during operation of component 14. Exterior surface 50B bounds a high-temperature environment having gas or fluid at static pressure P2 and temperature T2 in which temperature T2 exceeds temperature T1. A differential pressure across exterior wall 50 of component 14 (i.e., P1-P2) drives cooling fluid from interior cavity 54 through aperture 48 to discharge across exterior surface 50B at mass flow rate M.

[0019] In some examples, component 14 can include multiple apertures 48 arranged in an array and / or a group. Within an array, apertures 48 can be spaced along any one or more orthogonal axes (e.g., spacing S1 along axis Z and spacing S2 along axis Z) to form one or more rows and / or one or more columns of apertures. Some rows and / or columns can be offset relative to one or more other rows and / or columns, for example an adjacent row or an adjacent column, to form a staggered array of apertures. In other examples, apertures 48 can be arranged in groups in which geometry of apertures within groups are selected independently from other groups. For example, aperture geometry, aperture spacing, and / or aperture pattern can be different from aperture geometry, aperture spacing, and / or aperture pattern of another group of apertures.

[0020] FIG. 3 is a cross-sectional view of component 14 depicting one of apertures 48 from FIG. 2. As depicted, aperture 48 is inclined with respect to exterior surface 50B of component 14 at inclination angle A. While inclination angle A is depicted in the cross-sectional plane, inclination angle A can include an out-of-plane component such that inclination angle A in two or more planes represented by coordinate system 10, or other coordinate system with three mutually orthogonal axes in other examples of component 14.

[0021] In some manufacturing processes, formation of aperture 48 imparts deformation to component such that position, orientation, and / or diameter of aperture 48 may differ from an intended position, orientation, and / or diameter of aperture 48. For example, dashed lines 56 depicted in FIG. 3 represent an exaggerated through-thickness profile of aperture 48 that may result from a laser drilling process. Accordingly, mass flow M can deviate from a theoretical value as a result of the manufacturing process. Depending on the manufacturing process, the geometric deformation can be different, for example in case of laser drilling, multiple shots cause multiple small explosions inside the base metal and creates lateral deformation. This lateral deformation creates additional turbulence during the engine operation hence cooling effect deviates from optimal. Other factors contributing to deviations of mass flow M include position error, orientation error, thermal deformation from subsequent manufacturing processes such as coating application via chemical vapor plasma deposition and similar processes.

[0022] Aperture 48 has geometry defined by one or more geometric dimensions. Each geometric dimension is associated with a geometric variance that defines a range of values about a nominal geometric dimension. The range of values for each geometric dimension accounts for geometric dimensions that are larger than and / or smaller than the nominal geometric value. Further, the nominal geometric dimension can be the average value with the range for some geometric dimensions. In other example geometric dimensions, the nominal geometric dimension can be any value within the range defined by the geometric variance such as the maximum value or the minimum value.

[0023] FIG. 4 is a flow chart depicting method 100 for manufacturing component 14 with optimized aperture geometry and manufacturing input variables. The sequence depicted is for illustrative purposes only and is not meant to limit the method 100 in any way as it is understood that the portions of method 100 can proceed in a different logical order, additional or intervening portions can be included, or described portions of method 100 can be divided into multiple portions, or described portions of method 100 can be omitted without detracting from the described above. Method 100 includes steps 102, 104, 106, 108, and 110.

[0024] In step 102, a target mass flow rate of cooling fluid is determined for one or more apertures 48 of component 14. The target mass flow rate of cooling fluid can be associated with a single aperture 48, an array of apertures 48, or a group of apertures 48 of component 14. The target mass flow rate can be based on a nominal geometry of aperture 48, or each aperture 48 with an array or group.

[0025] In step 104, nominal geometry and one or more geometric variances of aperture 48 are determined based on the target mass flow rate of cooling fluid determined in step 102. Nominal geometry and geometric variances of aperture 48 can include, but are not limited aperture diameter, inclination, number, spacing, array pattern, and / or grouping, among other potential geometric dimensions and parameters. Example methods for determining the target mass flow rate, nominal aperture geometry, and geometric variances can include a model of component 14 (e.g., a finite element model), among other determinative methods executed by computing device 12 of system 10.

[0026] In step 106, an optimum geometry of the aperture 48 is determined based on the plurality of target geometry variances and the target mass flow rate. For example, determining the aperture's optimum geometry can include randomly selecting the plurality of plurality of geometric dimensions, each geometric dimension selection bound by a respective geometric variance. That is to say, each geometric dimension is selected from a range of values defined by the geometric variance. In further examples of method 100, step 106 can include randomly selecting geometric dimensions of aperture 48 multiple times using a Monte Carlo method in which sets of randomly selected dimensions define an even distribution of each geometric dimension within each geometric variance. Computing device 12 of system 10 can be used in some examples to randomly select geometric dimensions for aperture 48.

[0027] The optimum geometry of aperture 48 is determined by evaluating multiple apertures 48, each aperture 48 having a set of randomly selected geometric dimensions. For example, finite element modeling or other deterministic model can be used by computing device 12 to determine mass flow rate M of cooling fluid for each aperture 48 with randomly selected geometry. In another example, test specimens representative of the component with one or more of apertures 48, each aperture 48 having randomly selected geometry, can be used to empirically measure mass flow rate of cooling fluid flowing through each aperture 48 for the given differential pressure and cooling fluid properties. In other examples, a combined approach can be used in which some aperture geometries are evaluated using a deterministic model while test specimens are used to confirm mass flow rate and / or to measure mass flow rate through at least some aperture geometries. In each instance, the aperture geometry producing the mass flow rate M that equals or differs the least from the target mass flow rate can be selected as the optimum geometry.

[0028] In step 108, input variables 32 for manufacturing tool 16 are determined. For example, input variables 32 can be determined by selecting an initial set of input variables for manufacturing tool 16 and selecting a range for each input variable, the range determined based on accuracy characteristics of manufacturing tool 16. Subsequently, a set of input variables can be randomly selected from input variables within respective ranges. Each set of input variables is evaluated for achieving the optimal geometry. For example, multiple test specimens can be created with manufacturing tool 16, each specimen producing using one of the sets of input variables. In another example, a digital twin of manufacturing tool 16 can be created based on empirical data generated from multiple component specimens and / or by as-built geometry of one or more components 14. Each component specimen and as-built component can represent an aperture, an aperture array, or an aperture group of a particular component. Additionally, component specimens can include different wall thicknesses, materials, and / or different applied coating schemes. Aperture geometry of component specimens and as-built components can be measured using a coordinate measurement machine (CMM), for example, among other potential inspection systems and methods. Trained on data measured from component specimens and / or as-built components, the digital twin model can be used to estimate aperture geometry given a set of input variables. In each case, the set of input variables producing aperture 48 equivalent to the optimum geometry or with minimum deviation from the optimum geometry can be identified and used for subsequent manufacture of component 14.

[0029] In step 110, the optimum input variables are provided to manufacture tool 16, which are used to manufacture component 14 with optimum aperture geometry.Discussion of Possible Embodiments

[0030] The following are non-exclusive descriptions of possible embodiments of the present invention.A method for manufacturing a component with optimal aperture geometry

[0031] A method for manufacturing a component comprising an interior cavity and an aperture extending through an exterior wall of the component to intersect the interior cavity according to an example embodiment of this disclosure, among other possible things, includes determining a target mass flow rate through the aperture. The method further includes determining a plurality of target geometric variances of the aperture, each geometric variance associated with a plurality of geometric dimensions of the aperture. The method further includes identifying an optimum geometry of the aperture based on the plurality of target geometric variances and the target mass flow rate. The method further includes determining a plurality of input variables to a manufacturing tool corresponding to the optimum geometry. The method further includes manufacturing the component based on the plurality of input variables using the manufacturing tool.

[0032] The method of the preceding paragraph can optionally include, additionally and / or alternatively, any one or more of the following features, configurations and / or additional components.

[0033] A further embodiment of the foregoing method, wherein identifying the optimum geometry of the aperture can include randomly selecting the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances.

[0034] A further embodiment of any of the foregoing methods, wherein the plurality of geometric dimensions can be randomly selected using a Monte Carlo method.

[0035] A further embodiment of any of the foregoing methods, wherein identifying the optimum geometry of the aperture can further include determining a predicted mass flow through the test aperture.

[0036] A further embodiment of any of the foregoing methods, wherein identifying the optimum geometry of the aperture can further include determining the predicted mass flow through the test aperture using a model of the component.

[0037] A further embodiment of any of the foregoing methods, wherein the plurality of geometric dimensions of the aperture can include aperture diameter and aperture inclination relative to the exterior wall of the component.

[0038] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can include determining a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables.

[0039] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can include determining the plurality of input variables by selecting each input variable from respective ranges.

[0040] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can further include randomly selecting the plurality of input variables from respective ranges of the plurality of ranges.

[0041] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can further include determining a plurality of resultant apertures of the component based on the randomly selected input variables.

[0042] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can further include selecting the plurality of input variables from among the randomly selected input variables producing that produces a resultant aperture with minimized geometric variation with the optimum geometry.

[0043] A further embodiment of any of the foregoing methods, wherein determining the plurality of input variables to a manufacturing tool can further include outputting the plurality of input variables by a model of the manufacturing tool based on the optimum geometry.A system for manufacturing a component with optimal aperture geometry

[0044] A system for manufacturing a component comprising an interior cavity and an aperture extending through an exterior wall of the component to intersect the interior cavity according to an example embodiment of this disclosure, among other possible things, includes a manufacturing tool and a computing device. The manufacturing tool includes a plurality of input variables. The computing device includes a processor and memory encoded with instructions that, when executed by the processor, cause the system to determine, by the computing device, a target mass flow rate through the aperture. The instructions further cause the system to determine, by the computing device, a plurality of target geometric variances of the aperture, each geometric variance associated with a plurality of geometric dimensions of the aperture. The instructions further cause the system to identify, by the computing device, an optimum geometry of the aperture based on the plurality of target geometric variances and the target mass flow rate. The instructions further cause the system to determine, by the computing device, the plurality of input variable to the manufacturing tool corresponding to the optimum geometry and manufacture the component, using the manufacturing tool. using the plurality of input variable.

[0045] The system of the preceding paragraph can optionally include, additionally and / or alternatively, any one or more of the following features, configurations and / or additional components.

[0046] A further embodiment of the foregoing system, wherein the memory can be further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to randomly select, by the computing device, the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances.

[0047] A further embodiment of any of the foregoing systems, wherein the plurality of geometric dimensions can be randomly selected using a Monte Carlo method.

[0048] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to determine, by the computing device, a predicted mass flow through the test aperture.

[0049] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to determine, by the computing device, the predicted mass flow through the test aperture using a model of the component.

[0050] A further embodiment of any of the foregoing systems, wherein the plurality of geometric dimensions of the aperture can include aperture diameter and aperture inclination relative to the exterior wall of the component.

[0051] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to determine, by the computing device, a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables.

[0052] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to determine, by the computing device, the plurality of input variables by selecting each input variable from respective ranges.

[0053] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to randomly select, by the computing device, the plurality of input variables from respective ranges of the plurality of ranges.

[0054] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to determine, by the computing device, a plurality of resultant apertures of the component based on the randomly selected input variables.

[0055] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to select, by the computing device, the plurality of input variables from among the randomly selected input variables producing that produces a resultant aperture with minimized geometric variation with the optimum geometry.

[0056] A further embodiment of any of the foregoing systems, wherein the memory can be further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to output, by the computing device, the plurality of input variables by a model of the manufacturing tool based on the optimum geometry.

[0057] While the invention has been described with reference to an exemplary embodiment(s), it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment(s) disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims

1. A method for manufacturing a component comprising an interior cavity and an aperture extending through an exterior wall of the component to intersect the interior cavity, the method comprising: determining a target mass flow rate through the aperture; determining a plurality of target geometric variances of the aperture, each geometric variance associated with a plurality of geometric dimensions of the aperture; identifying an optimum geometry of the aperture based on the plurality of target geometric variances and the target mass flow rate; determining a plurality of input variables to a manufacturing tool corresponding to the optimum geometry; and manufacturing the component based on the plurality of input variables using the manufacturing tool.

2. The method of claim 1, wherein identifying the optimum geometry of the aperture includes: randomly selecting the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances.

3. The method of claim 2, wherein the plurality of geometric dimensions is randomly selected using a Monte Carlo method.

4. The method of claim 3, wherein identifying the optimum geometry of the aperture further includes: determining a predicted mass flow through the test aperture.

5. The method of claim 4, wherein identifying the optimum geometry of the aperture further includes: determining the predicted mass flow through the test aperture using a model of the component, optionally, wherein the plurality of geometric dimensions of the aperture includes aperture diameter and aperture inclination relative to the exterior wall of the component.

6. The method of any preceding claim, wherein determining the plurality of input variables to a manufacturing tool includes: determining a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables; and determining the plurality of input variables by selecting each input variable from respective ranges.

7. The method of claim 6, wherein determining the plurality of input variables to a manufacturing tool further includes: randomly selecting the plurality of input variables from respective ranges of the plurality of ranges; determining a plurality of resultant apertures of the component based on the randomly selected input variables; and selecting the plurality of input variables from among the randomly selected input variables producing that produces a resultant aperture with minimized geometric variation with the optimum geometry; and / or wherein determining the plurality of input variables to a manufacturing tool further includes: outputting the plurality of input variables by a model of the manufacturing tool based on the optimum geometry.

8. The method of any preceding claim, wherein identifying the optimum geometry of the aperture includes: randomly selecting the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances; and wherein determining the plurality of input variables to a manufacturing tool includes: determining a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables; and determining the plurality of input variables by selecting each input variable from respective ranges.

9. A system for manufacturing a component comprising an interior cavity and an aperture extending through an exterior wall of the component to intersect the interior cavity, the system comprising: a manufacturing tool comprising a plurality of input variables; and a computing device comprising a processor and memory encoded with instructions that, when executed by the processor, cause the system to: determine, by the computing device, a target mass flow rate through the aperture; determine, by the computing device, a plurality of target geometric variances of the aperture, each geometric variance associated with a plurality of geometric dimensions of the aperture; identify, by the computing device, an optimum geometry of the aperture based on the plurality of target geometric variances and the target mass flow rate; determine, by the computing device, the plurality of input variables to the manufacturing tool corresponding to the optimum geometry; and manufacture the component using the manufacturing tool based on the plurality of input variables.

10. The system of claim 9, wherein the memory is further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to: randomly select, by the computing device, the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances.

11. The system of claim 10, wherein the plurality of geometric dimensions is randomly selected using a Monte Carlo method.

12. The system of claim 11, wherein the memory is further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to: determine, by the computing device, a predicted mass flow through the test aperture, optionally, wherein the memory is further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to: determine, by the computing device, the predicted mass flow through the test aperture using a model of the component, optionally, wherein the plurality of geometric dimensions of the aperture includes aperture diameter and aperture inclination relative to the exterior wall of the component.

13. The system of any of claims 9 to 12, wherein the memory is further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to: determine, by the computing device, a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables; and determine, by the computing device, the plurality of input variables by selecting each input variable from respective ranges.

14. The system of claim 13, wherein the memory is further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to: randomly select, by the computing device, the plurality of input variables from respective ranges of the plurality of ranges; determine, by the computing device, a plurality of resultant apertures of the component based on the randomly selected input variables; and select, by the computing device, the plurality of input variables from among the randomly selected input variables producing that produces a resultant aperture with minimized geometric variation with the optimum geometry; and / or wherein the memory is further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to: output, by the computing device, the plurality of input variables by a model of the manufacturing tool based on the optimum geometry.

15. The system of any of claims 9 to 14, wherein the memory is further encoded with instructions that when executed by the processor to identify the optimum geometry cause the system to: randomly select, by the computing device, the plurality of geometric dimensions of the aperture to define a test aperture, each dimension of the plurality of dimensions bound by respective geometric variances of the plurality of geometric variances; and wherein the memory is further encoded with instructions that, when executed by the processor to determine the plurality of input variables, cause the system to: determining a plurality of ranges, each range corresponding to one of the input variables of the plurality of input variables; and determining the plurality of input variables by selecting each input variable from respective ranges.

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