METHOD FOR OPTIMIZING A MANUFACTURING PROCESS

Abstract

A method for optimizing a manufacturing process includes arranging a finite element method simulation model, a microstructure model, and a material model in a closed loop. The method includes performing an iterative simulation process to determine a simulated output of the manufacturing process. The iterative simulation process includes performing a plurality of iterations. Each of the plurality of iterations includes the steps of: determining, by the finite element method simulation model, a plurality of thermomechanical parameters based on a plurality of process parameters and a plurality of previous predicted material properties; determining, by the microstructure model, a predicted change in a microstructure based on the plurality of thermomechanical parameters; and determining, by the material model, a plurality of current predicted material properties based on the predicted change in the microstructure.

Claims

1. A method for optimizing a manufacturing process, the method comprising the steps of: arranging a finite element method simulation model, a microstructure model, and a material model in a closed loop; and performing an iterative simulation process to determine a simulated output of the manufacturing process, the iterative simulation process comprising performing a plurality of iterations, the plurality of iterations comprising a first iteration and a last iteration, each of the plurality of iterations comprising the steps of: determining, by the finite element method simulation model, a plurality of thermomechanical parameters of a component undergoing the manufacturing process based on a plurality of process parameters of the manufacturing process and a plurality of previous predicted material properties of the component; determining, by the microstructure model, a predicted change in a microstructure of the component based on the plurality of thermomechanical parameters determined by the finite element method simulation model; and determining, by the material model, a plurality of current predicted material properties of the component based on the predicted change in the microstructure of the component determined by the microstructure model; and determining a quality of the plurality of the process parameters based on the simulated output.

2. The method of claim 1, wherein the simulated output comprises at least a plurality of final thermomechanical parameters of the component determined in the last iteration from the plurality of iterations of the iterative simulation process.

3. The method of claim 1, wherein, in each iteration except the first iteration, the plurality of previous predicted material properties of the component is determined by the material model in a previous iteration from the plurality of iterations.

4. The method of claim 1, wherein each iteration further comprises providing the plurality of current predicted material properties as the plurality of previous predicted material properties to the finite element method simulation model in a next iteration from the plurality of iterations.

5. The method of claim 1, wherein the plurality of previous predicted material properties comprises at least a predicted flow stress of the component.

6. The method of claim 1, wherein the plurality of thermomechanical parameters comprises a temperature and a stress of the component.

7. The method of claim 1, wherein the microstructure comprises a gamma-prime microstructure of the component.

8. The method of claim 1, wherein the component includes at least one of a nickel alloy, an aluminium alloy, and a titanium alloy.

9. The method of claim 1, wherein the manufacturing process is at least one of a welding process, a forging process, and a cutting process.

10. The method of claim 9, wherein the welding process is an inertia friction welding process.

11. The method of claim 10, wherein the plurality of process parameters comprises at least one of a flywheel energy, a flywheel rotational speed, and a forging pressure.

12. The method of claim 1, wherein the plurality of iterations is performed for a plurality of time steps.

13. The method of claim 1, further comprising terminating the iterative simulation process after a predetermined time duration.

14. The method of claim 1, further comprising, prior to performing the iterative simulation process: receiving the plurality of process parameters and a plurality of initial material properties of the component; and determining, by the finite element method simulation model, a plurality of initial thermomechanical parameters to initiate the iterative simulation process.

15. The method of claim 14, wherein the plurality of initial material properties is provided as the plurality of previous predicted material properties of the component to the finite element method simulation model in the first iteration.

16. The method of claim 1, wherein the quality of the plurality of process parameters comprises at least a good quality or a bad quality.

17. The method of claim 16, further comprising applying the process parameters to manufacture the component if the quality of the plurality of process parameters is the good quality.

18. A computing device comprising a processor and a memory having stored therein a plurality of instructions that when executed by the processor causes the computing device to perform the method of claim 1.

19. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause at least one processor to perform the method of claim 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Embodiments will now be described by way of example only, with reference to the figures, in which:

[0037] FIG. 1 shows a flowchart of a method for optimizing a manufacturing process;

[0038] FIG. 2A shows a schematic block diagram of a first iterative simulation process of the method shown in FIG. 1;

[0039] FIG. 2B shows a schematic block diagram of an iterative simulation process after FIG. 2A of the method shown in FIG. 1;

[0040] FIG. 2C show a schematic block diagram of a last iterative simulation process of the method shown in FIG. 1;

[0041] FIG. 3 shows a schematic block diagram of a computing device; and

[0042] FIG. 4 shows a schematic view of a simulated output of the iterative simulation process.

DETAILED DESCRIPTION

[0043] Aspects and embodiments of the present disclosure will now be discussed with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art.

[0044] FIG. 1 shows a flowchart of a method 100 for optimizing a manufacturing process, according to an embodiment of the present disclosure.

[0045] In some embodiments, the manufacturing process is at least one of a welding process, a forging process, and a cutting process. In some embodiments, the welding process is an inertia friction welding process. The manufacturing process may be used to manufacture a component C (shown in FIG. 4). In some embodiments, the component C is manufactured according to the method 100.

[0046] In some embodiments, the component C includes at least one of a nickel alloy, an aluminium alloy, and a titanium alloy. In some embodiments, the component C is for a gas turbine engine (not shown). In some embodiments, the component C is a high-pressure compressor drum of the gas turbine engine. However, in some other embodiments, the component C may include any component of the gas turbine engine.

[0047] FIG. 2A, FIG. 2B and FIG. 2C show schematic block diagrams of the method 100 shown in FIG. 1, according to an embodiment of the present disclosure. Specifically, FIG. 2A shows a schematic block diagram of a first iteration 212F of an iterative simulation process 200 of the method 100, FIG. 2B shows a schematic block diagram of the iterative simulation process 200 after the first iteration 212F (shown in FIG. 2A) except a last iteration 212L (shown in FIG. 2C), and FIG. 2C shows a schematic block diagram of the last iteration 212L of the iterative simulation process 200.

[0048] Referring to FIG. 1, FIG. 2A, FIG. 2B and FIG. 2C, at step 102, the method 100 includes arranging a finite element method (FEM) simulation model 206, a microstructure model 208, and a material model 210 in a closed loop.

[0049] At step 104, the method 100 includes performing the iterative simulation process 200 to determine a simulated output 220 (shown in FIG. 2C and FIG. 4) of the manufacturing process.

[0050] The iterative simulation process 200 includes performing a plurality of iterations 212. The plurality of iterations 212 includes the first iteration 212F (shown in FIG. 2A) and the last iteration 212L (shown in FIG. 2C).

[0051] In some embodiments, the method 100 further includes, prior to performing the iterative simulation process 200, receiving a plurality of process parameters 204 and a plurality of initial material properties 205 of the component C.

[0052] In some embodiments, the plurality of process parameters 204 includes at least one of a flywheel energy, a flywheel rotational speed, and a forging pressure. Specifically, as discussed above, in some embodiments, the welding process is the inertia friction welding process. In such embodiments, the plurality of process parameters 204 includes at least one of the flywheel energy, the flywheel rotational speed, and the forging pressure which may be strongly linked to a rate of heating and deformation of the component C during the manufacturing process.

[0053] In some embodiments, the plurality of initial material properties 205 includes at least an initial flow stress of the component C. A flow stress may determine how the component C behaves and deforms during the manufacturing process.

[0054] Specifically, at block 202, the plurality of process parameters 204 of the manufacturing process and the plurality of initial material properties 205 of the component C are provided as inputs for the iterative simulation process 200.

[0055] In some embodiments, the method 100 further includes, prior to performing the iterative simulation process 200, determining, by the FEM simulation model 206, a plurality of initial thermomechanical parameters 215 to initiate the iterative simulation process 200. In some embodiments, the plurality of initial material properties 205 is provided as a plurality of previous predicted material properties 218P (shown in FIG. 2B) of the component C to the FEM simulation model 206 in the first iteration 212F.

[0056] Therefore, upon receiving the plurality of process parameters 204 of the manufacturing process and the plurality of initial material properties 205 of the component C, in the first iteration 212F, the FEM simulation model 206 may determine the plurality of initial thermomechanical parameters 215 to initiate the iterative simulation process 200.

[0057] Each of the plurality of iterations 212 (shown in FIG. 2B) includes steps 106, 108, 110 of the method 100 shown in FIG. 1.

[0058] At step 106, each of the plurality of iterations 212 includes determining, by the FEM simulation model 206, a plurality of thermomechanical parameters 214 of the component C undergoing the manufacturing process based on the plurality of process parameters 204 of the manufacturing process and the plurality of previous predicted material properties 218P of the component C. In some embodiments, the plurality of thermomechanical parameters 214 includes a temperature and a stress of the component C.

[0059] In some embodiments, in each iteration 212 except the first iteration 212F, the plurality of previous predicted material properties 218P of the component C is determined by the material model 210 in a previous iteration from the plurality of iterations 212. In some embodiments, the plurality of previous predicted material properties 218P includes at least a predicted flow stress of the component C. The predicted flow stress of the component C may help to predict how the component C may behave and deform during the manufacturing process.

[0060] At step 108, each of the plurality of iterations 212 includes determining, by the microstructure model 208, a predicted change 216C in a microstructure 216 of the component C based on the plurality of thermomechanical parameters 214 determined by the FEM simulation model 206.

[0061] In some embodiments, in the first iteration 212F, the plurality of initial thermomechanical parameters 215 are provided to the microstructure model 208 as the plurality of thermomechanical parameters 214.

[0062] At step 110, each of the plurality of iterations 212 includes determining, by the material model 210, a plurality of current predicted material properties 218C of the component C based on the predicted change 216C in the microstructure 216 of the component C determined by microstructure model 208.

[0063] In some embodiments, each iteration 212 further includes providing the plurality of current predicted material properties 218C as the plurality of previous predicted material properties 218P to the FEM simulation model 206 in a next iteration from the plurality of iterations 212.

[0064] In some embodiments, the microstructure 216 includes the gamma-prime microstructure y (shown in FIG. 4) of the component C. The gamma-prime microstructure y of the component C may include precipitates which dissolve dynamically (e.g., over time and dependent on instantaneous thermomechanical parameters 214, such as the temperature and the stress), leading to a rate of softening dependent on a rate they are exposed to the thermomechanical parameters 214. Further, the current predicted material properties 218C (e.g., the plasticity and the flow stress) of the component C may be based on, amongst other things, a dislocation motion which interacts with the precipitates. Therefore, the method 100 may take into account the instantaneous or current predicted change 216C in the gamma-prime microstructure y.

[0065] In some embodiments, the plurality of iterations 212 is performed for a plurality of time steps. In some embodiments, the method 100 includes terminating the iterative simulation process 200 after a predetermined time duration. The method 100 predicts the changes (i.e., the predicted change 216C) in the microstructure 216 and the material properties (i.e., the current predicted material properties 218C) at each time step and provides the simulated output 220 after the plurality of time steps. Thus, the iterative simulation process 200 may be performed numerous times to provide the simulated output 220 before terminating.

[0066] In some embodiments, the simulated output 220 includes at least a plurality of final thermomechanical parameters 214L of the component C determined in the last iteration 212L from the plurality of iterations 212 of the iterative simulation process 200. In some embodiments, the simulated output 220 includes at least a final predicted change 216L in the microstructure 216 of the component C determined in the last iteration 212L from the plurality of iterations 212 of the iterative simulation process 200. In some embodiments, the simulated output 220 includes a plurality of final predicted material properties 218L of the component C determined in the last iteration 212L from the plurality of iterations 212 of the iterative simulation process 200.

[0067] At step 112, the method 100 includes determining a quality of the plurality of the process parameters 204 based on the simulated output 220. In some embodiments, the quality of the plurality of process parameters 204 includes at least a good quality or a bad quality. The method 100 may therefore enable a user to predict if a result (e.g., based on the simulated output 220) of the manufacturing process would be suitable or not for applying to the manufacturing process.

[0068] Furthermore, in some embodiments, the method 100 includes applying the process parameters 204 to manufacture the component C if the quality of the plurality of process parameters 204 is the good quality. Therefore, the method 100 may provide right first-time process parameters, which may lead to less trial-and-error, scrap avoidance, and shorter development lead-time.

[0069] In some embodiments, the FEM simulation model 206 may be implemented in Abaqus solver. In some embodiments, the microstructure model 208 and the material model 210 may be implemented in Fortran subroutines. In some embodiments, the inputs (i.e., the plurality of process parameters 204 of the manufacturing process and the plurality of initial material properties 205 of the component C) may be provided using Python job-handling scripts. Further, the simulated output 220 may also be post-processed using the Python job-handling scripts.

[0070] In some embodiments, the FEM simulation model 206 may use the initial/previously predicted flow stresses, and the process parameters, such as applied external loads, surface friction, and the flywheel energy to calculate displacement of each finite element (FE).

[0071] In some embodiments, the microstructure model 208 updates the microstructure 216 to the predicted change 216C in the microstructure 216 depending upon the thermomechanical parameters 214 (e.g., local thermal conditions).

[0072] In some embodiments, the material model 210 calculates a plastic strain rate from local microstructure state and the resultant stress (e.g., the flow stress) is determined.

[0073] FIG. 3 shows a schematic block diagram of a computing device 10, according to an embodiment of the present disclosure.

[0074] The computing device 10 includes a processor 12 and a memory 14. The processor 12 and the memory 14 are communicably coupled to each other. The memory 14 has stored therein a plurality of instructions 15 that when executed by the processor 12 causes the computing device 10 to perform the method 100 shown in FIG. 1. In some embodiments, a non-transitory computer-readable storage medium (e.g., the memory 14) includes the instructions 15 that, when executed, cause at least one processor (e.g., the processor 12) to perform the method 100 shown in FIG. 1.

[0075] In some embodiments, the computing device 10 may further includes a user interface (not shown) communicably coupled to the processor 12. In some embodiments, the user interface is a graphical user interface. In some examples, the user interface may include a keyboard, a mouse, a display, and the like. The user may interact with the user interface to enter the inputs information (i.e., the plurality of process parameters 204 of the manufacturing process and the plurality of initial material properties 205 of the component C) or to obtain an output information (e.g., the simulated output 220).

[0076] FIG. 4 shows the simulated output 220 of the iterative simulation process 200 shown in FIG. 2A, FIG. 2B, FIG. 20, according to an embodiment of the present disclosure.

[0077] In the illustrated example of FIG. 4, the simulated output 220 includes the plurality of final thermomechanical parameters 214L which may be a process model including a final temperature T and/or a final stress (not shown), the final predicted change 216L in the microstructure 216 of the component C which may be a gamma prime microstructure evolution, and the plurality of final predicted material properties 218L of the component C which may be a final flow stress FS.

[0078] As illustrated in the embodiment of FIG. 4, the simulated output 220 may be graphically represented by the user interface of the computing device 10 shown in FIG. 3. In some embodiments, the user may interact with the simulated output 220.

[0079] In some embodiments, the simulated output 220 includes the plurality of final thermomechanical parameters 214L for different locations of the component C. The different locations may have different thermomechanical parameters 214, for example, different final temperatures T. Specifically, locations A and B may have different final temperatures T. In the illustrated embodiment of FIG. 4, the plurality of final thermomechanical parameters 214L for different locations of the component C is graphically represented.

[0080] Since the microstructure 216 of the component C is dependent upon the thermomechanical parameters 214, different locations of the component C may have different microstructures.

[0081] In some embodiments, the simulated output 220 includes the final predicted change 216L in the microstructure 216 for different locations (e.g., for the locations A and B) of the component C. For example, the location A on the component C may have a final predicted microstructure MA and the location B on the component C may have a final predicted microstructure MB.

[0082] Further, since the material properties (e.g., the current predicted material properties 218C) of the component C is dependent upon the predicted change (e.g., the predicted change 216C) in the microstructure 216, different locations e.g., for the locations A and B) of the component C may have different material properties.

[0083] In some embodiments, the simulated output 220 includes the final predicted material properties 218L for different locations of the component C. For example, the location A on the component C may have material properties depicted by a curve MPA and the location B on the component C may have material properties depicted by a curve MPB.

[0084] It will be understood that the invention is not limited to the embodiments above-described and various modifications and improvements can be made without departing from the concepts described herein. Except where mutually exclusive, any of the features may be employed separately or in combination with any other features and the disclosure extends to and includes all combinations and sub-combinations of one or more features described herein.