Method and system for optimizing a manufacturing process based on a surrogate model of a part
There is provided a method for optimizing a manufacturing process of a new part. The method includes executing, by a system configured to drive the manufacturing process, a set of manufacturing functions. Executing these functions include receiving data associated with one or more field parts similar to the new part, and generating, based on the data, a forecast representative of a longevity of the one or more parts. The method further includes generating a digital thread forming a surrogate model for the new part, based on the forecast. Further, the method includes creating the set of manufacturing functions based on the surrogate model and manufacturing the new part according to the set of manufacturing functions.
1. A method for optimizing a manufacturing process of a new part, the method comprising:
executing, by a system configured to drive the manufacturing process, a set of manufacturing functions, the executing including:
receiving data associated with one or more fielded parts similar to the new part;
generating, based on the data, a forecast representative of a longevity of the one or more fielded parts;
generating a digital thread forming a surrogate model for the new part, based on the forecast;
creating the set of manufacturing functions based on the surrogate model; and
manufacturing the new part according to the set of manufacturing functions.
2. The method of claim 1 , wherein the surrogate model is associated with an engine component.
3. The method of claim 1 , wherein the surrogate model is associated with a component in a hot gas path of an engine or with a cold section of the engine.
4. The method of claim 1 , wherein the surrogate model is updated according to a machine learning model.
5. The method of claim 1 , wherein the set of manufacturing functions represent an optimized set of manufacturing functions relative to another of set of manufacturing functions that are not based on the surrogate model.
6. The method of claim 1 , further comprising executing analytics module configured to update the surrogate model via training.
7. The method of claim 6 , wherein the analytics module is configured to update the surrogate model based on a transfer function of a set of manufacturing parameters and a robustness of the one or more fielded parts.
8. The method of claim 6 , wherein the analytics module is configured to update the surrogate model based on a set of manufacturing parameters, laser settings, part rotation speeds, Electrical Discharge Machining (EDM) burn rates, and a robustness metric.
9. The method of claim 6 , wherein the analytics module is configured to update the surrogate model without using a physics-based model of the new part.
10. A system for optimizing a manufacturing process of a new part, the system comprising:
a processor;
a memory including instructions that, when executed by the processor, cause the processor to perform operations including:
executing a set of manufacturing functions, the executing including:
receiving data associated with one or more fielded parts similar to the new part;
generating, based on the data, a forecast representative of a longevity of the one or more fielded parts;
generating a digital thread forming a surrogate model for the new part, based on the forecast;
creating the set of manufacturing functions based on the surrogate model; and
manufacturing the new part according to the set of manufacturing functions.
11. The system of claim 10 , wherein the surrogate model is associated with an engine component.
12. The system of claim 10 , wherein the surrogate model is associated with a component of a hot gas path of an engine or with a cold section of the engine.
13. The system of claim 10 , wherein the surrogate model is associated with an engine component.
14. The system of claim 10 , wherein the surrogate model is associated with a subsystem of an engine.
15. The system of claim 10 , wherein the operations further include executing an analytics module including a machine learning module.
16. The system of claim 10 , wherein the set of manufacturing functions represent an optimized set of manufacturing functions relative to another of set of manufacturing functions that are not based on the surrogate model.
17. The system of claim 15 , wherein the analytics module is configured to update the surrogate model via training.
18. The system of claim 15 , wherein the analytics module is configured to update the surrogate model based on a transfer function of a set of manufacturing parameters and a robustness of the one or more fielded parts.
19. The system of claim 15 , wherein the analytics module is configured to update the surrogate model based on a set of manufacturing parameters, laser settings, part rotation speeds, Electrical Discharge Machining (EDM) burn rates, and a robustness metric.
20. The system of claim 15 , wherein the analytics module is configured to update the surrogate model without using a physics-based model of the new part.