Process control of a composite fabrication process

1. A system for process control of a composite fabrication process comprising:

an automated composite placement head configured to lay down composite material;

a vision system connected to the automated composite placement head and configured to produce image data during an inspection of the composite material, wherein the inspection takes place at least one of during or after laying down the composite material;

a computer system configured to identify inconsistencies in the composite material visible within the image data, and make a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and

a display, wherein the computer system is configured to show the image data on the display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.

2. The system of claim 1 , wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material.

3. The system of claim 2 , wherein the computer system is configured to make the number of metrology decisions while the automated composite placement head is laying down the composite material.

4. The system of claim 1 , wherein the number of metrology decisions comprises modifying an inconsistency allowance threshold, wherein the computer system is configured to employ a probabilistic approach to modify the inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on at least one property of the inconsistencies identified in the image data, wherein properties of inconsistencies include at least one of size, density, location, inconsistency type, or randomness.

5. The system of claim 4 , wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.

6. The system of claim 1 , wherein the composite material is a part of a component, wherein the computer system is configured to compare locations of the inconsistencies identified in the image data to a design of the component.

7. The system of claim 1 , wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.

8. A method comprising:

automatically imaging a composite material, during or after laying down the composite material, using a vision system to form image data;

identifying, by a computer system, inconsistencies in the composite material visible within the image data in real-time;

making, by the computer system, a number of metrology decisions automatically, without operator intervention, based on the inconsistencies; and

displaying the image data in real-time with a width and a length superimposed over each of the inconsistencies that is visible within displayed image data.

9. The method of claim 8 further comprising:

storing data for the inconsistencies in a database;

building machine learning datasets and probabilistic information using the database; and

using the machine learning datasets and the probabilistic information to forecast a quality of a portion of a component containing the composite material.

10. The method of claim 8 , wherein making the number of metrology decisions includes:

sending out a warning when an inconsistency of the inconsistencies identified in the image data violates an inconsistency allowance threshold.

11. The method of claim 10 , wherein the composite material is part of a component, wherein the inconsistency allowance threshold takes into account at least one of a quantity of inconsistencies identified in a prior level of composite material of the component, types of inconsistencies identified in a prior level of composite material of the component, or locations of inconsistencies identified in a prior level of composite material of the component.

12. The method of claim 8 , wherein making the number of metrology decisions includes:

modifying an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on properties of the inconsistencies identified in the image data including at least one of locations of the inconsistencies, a quantity of the inconsistencies, a density of the inconsistencies, or a measure of randomness of the inconsistencies.

13. The method of claim 12 , wherein the composite material is part of a component, and wherein the inconsistency allowance threshold is modified based on a design of the component.

14. The method of claim 13 , wherein the inconsistency allowance threshold is modified based on historical performance data of other components.

15. The method of claim 8 further comprising:

assigning an inconsistency type, by the computer system, to each of the inconsistencies identified in the image data.

16. The method of claim 8 further comprising:

measuring the inconsistencies identified in the image data.

17. The method of claim 8 , wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.

18. A method comprising:

creating image data of a composite material using a vision system, wherein the image data is created at least one of during or after laying down the composite material;

identifying in real-time, by a computer system, inconsistencies in the composite material visible within the image data; and

displaying the image data on a display in real-time with a width and a length superimposed over each of the inconsistencies that is visible within the image data on the display.

19. The method of claim 18 further comprising:

making, by the computer system, a number of metrology decisions based on the inconsistencies, historical performance data, and a design of a component wherein the composite material is a part of the component.

20. The method of claim 19 , wherein the number of metrology decisions comprises adjusting composite lay down parameters for the composite material or a future ply.

21. The method of claim 18 further comprising:

modifying, by the computer system, an inconsistency allowance threshold while imaging the composite material, wherein the inconsistency allowance threshold is modified based on the inconsistencies identified in the image data.

22. The method of claim 21 , wherein the inconsistency allowance threshold includes at least one of a quantity of total inconsistencies, a quantity of a specific type of inconsistencies, a size of an inconsistency, a size of a specific type of inconsistency, a density of inconsistencies, or a density of a specific type of inconsistencies.

23. The method of claim 18 , wherein the computer system is further configured to store data for the inconsistencies in a database, build machine learning datasets and probabilistic information using the database, and use the machine learning datasets and probabilistic information to forecast a quality of a portion of component containing the composite material.