Systems and methods for composite fabrication with AI quality control modules
A quality control system may include a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors. The controller may implement two or more AI quality control (AIQC) modules associated with two or more process steps for fabricating a composite material, where each of the two or more AIQC modules is associated with a different one of the two or more process steps. A particular AIQC module may receive monitoring data associated with the particular process step for a workpiece, generate quality control data using a particular AI model, and update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.
1. A quality control system comprising:
a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors, wherein the controller includes one or more processors configured to execute program instructions causing the one or more processors to implement two or more artificial intelligence quality control (AIQC) modules associated with two or more process steps of a plurality of process steps for fabricating a composite material from two or more plies, wherein each of the two or more AIQC modules is associated with a different one of the two or more process steps, wherein a particular one of the two or more AIQC modules associated with a particular process step of the two or more process steps is configured to:
receive monitoring data from the monitoring assembly associated with the particular process step for a workpiece, the workpiece including at least one of a mold or any of the two or more plies, wherein the monitoring data includes at least one of data associated with the workpiece or an operator associated with fabricating the composite material at the particular process step;
generate quality control data for the particular process step using a particular artificial intelligence (AI) model based on input data including at least the monitoring data associated with the particular process step, the quality control data including at least a pass indicator or a fail indicator for the particular process step, wherein the particular AI model is trained on a training dataset including at least additional monitoring data associated with the particular process step associated with additional workpieces labeled with the pass indicator and the fail indicator; and
update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.
2. The quality control system of claim 1 , wherein the program instructions are further configured to cause the one or more processors to:
at least one of train or update the particular AI model with monitoring data and associated testing data from one or more additional process steps of the plurality of process steps.
3. The quality control system of claim 1 , wherein the operator comprises a human operator.
4. The quality control system of claim 3 , wherein locations of quality issues are displayed on a human-machine interface.
5. The quality control system of claim 3 , wherein the program instructions are further configured to cause the processors to:
provide one or more quality control outputs associated with the quality control data for the particular process step to the operator for verification, the quality control outputs including at least the pass indicator or the fail indicator; and
receive a response from the operator including one of a verification or an override of the quality control outputs.
6. The quality control system of claim 5 , wherein the program instructions are further configured to cause the processors to:
update the particular AI model based on the response from the operator and the monitoring data associated with the particular process step.
7. The quality control system of claim 6 , wherein at least one of updating the particular AI model based on the response from the operator and the monitoring data associated with the particular process step or updating the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step is performed conditionally upon verification by a human user.
8. The quality control system of claim 5 , wherein completion of the particular process step requires one of:
the pass indicator by the particular AI model and the verification by the operator; or
the fail indicator by the particular AI model and the override by the operator.
9. The quality control system of claim 1 , wherein the operator comprises a robotic operator.
10. The quality control system of claim 1 , wherein the two or more process steps associated with the two or more AIQC modules comprise:
at least two of mold inspection, ply backing removal, foreign object detection, ply backing inspection, ply templating, ply orientation inspection, ply shaping, or de-bulk leak detection.
11. The quality control system of claim 5 , wherein at least one of the two or more AIQC modules is associated with ply backing removal, wherein the quality control data includes a presence of at least a portion of a ply backing on the workpiece.
12. The quality control system of claim 11 , wherein the one or more quality control outputs include an indication of a location of the at least a portion of the ply backing on the workpiece.
13. The quality control system of claim 5 , wherein at least one of the two or more AIQC modules is associated with foreign object detection, wherein the quality control data includes a presence of one or more foreign objects, the one or more foreign objects including at least one of materials or objects that deviate from a recipe describing the workpiece.
14. The quality control system of claim 13 , wherein the one or more quality control outputs include an indication of locations of the one or more foreign objects.
15. The quality control system of claim 13 , wherein the one or more foreign objects include at least one of dust, hair, or a portion of a ply backing material.
16. The quality control system of claim 13 , wherein the monitoring data includes hyperspectral image data with one or more images of the workpiece using one or more selected wavelengths.
17. The quality control system of claim 13 , wherein the monitoring data includes spectroscopic data.
18. The quality control system of claim 13 , wherein the particular AI model for foreign object detection identifies foreign objects on the workpiece using at least one of object detection, object classification, or a surface profile of the workpiece.
19. The quality control system of claim 5 , wherein at least one of the two or more AIQC modules is associated with ply orientation, wherein the quality control data includes an orientation of a weave pattern of a ply on the workpiece.
20. The quality control system of claim 19 , wherein at least one of the two or more AIQC modules is associated with ply conformance, wherein the quality control data includes a presence of one or more non-conformances of a ply on the workpiece.
21. The quality control system of claim 20 , wherein at least one of the one or more non-conformances comprises:
at least one of a wrinkle or a bridge in the ply on the workpiece.
22. The quality control system of claim 20 , wherein the one or more quality control outputs include an indication of locations of the one or more non-conformances.
23. The quality control system of claim 20 , wherein the monitoring data includes one or more images of the workpiece generated by infrared illumination, wherein the monitoring data includes flash thermography data.
24. The quality control system of claim 1 , wherein at least one of the two or more AIQC modules is associated with a de-bulk lead detection process step, wherein the quality control data includes a presence of a leak in a bag containing the workpiece.
25. The quality control system of claim 19 , wherein at least one of the two or more AIQC modules is associated with a mold inspection process step.
26. The quality control system of claim 25 , wherein the one or more quality control outputs include an indication of a presence or quality of a release agent.
27. The quality control system of claim 25 , wherein the one or more quality control outputs include a surface quality of the mold.
28. The quality control system of claim 25 , wherein the monitoring data includes hyperspectral image data with one or more images of the workpiece using one or more selected wavelengths.
29. The quality control system of claim 25 , wherein the monitoring data includes spectroscopic data.
30. The quality control system of claim 5 , wherein at least one of the one or more quality control outputs for at least one of the two or more AIQC modules comprises:
operator-specific instructions for the operator of the particular process step based on the quality control data for the particular process step for one or more previous workpieces associated with the operator.
31. The quality control system of claim 5 , wherein at least one of the one or more quality control outputs for at least one of the two or more AIQC modules comprises:
locations of quality issues identified based on the quality control data on the workpiece.
32. The quality control system of claim 31 , wherein the locations of quality issues are provided as patterns projected onto the workpiece.
33. The quality control system of claim 1 , wherein at least one of the one or more testing tools comprises:
an ultrasonic testing tool.
34. The quality control system of claim 1 , wherein the program instructions are further configured to cause the one or more processors to:
modify at least one of the plurality of process steps for fabricating the composite material based on at least one of the monitoring data, the quality control data, or the testing data.
35. A quality control method comprising:
fabricating a composite material from two or more plies using a plurality of process steps by one or more operators; and
implementing two or more artificial intelligence quality control (AIQC) modules for two or more process steps of the plurality of process steps, wherein each of the two or more AIQC modules is associated with a different one of the two or more process steps, wherein a particular one of the two or more AIQC modules associated with a particular process step of the two or more process steps is configured for:
receiving monitoring data from a monitoring assembly associated with the particular process step for a workpiece, the workpiece including at least one of a mold or any of the two or more plies, wherein the monitoring data includes at least one of data associated with the workpiece or a corresponding operator of the one or more operators associated with the particular process step;
generating quality control data for the particular process step using a particular artificial intelligence (AI) model based on input data including at least the monitoring data associated with the particular process step, the quality control data including at least a pass indicator or a fail indicator for the particular process step, wherein the particular AI model is trained on a training dataset including at least additional monitoring data associated with the particular process step associated with additional workpieces labeled with the pass indicator and the fail indicator;
providing one or more quality control outputs associated with the quality control data for the particular process step to the corresponding operator for verification, the one or more quality control outputs including at least the pass indicator or the fail indicator; and
updating the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.
36. The quality control method of claim 35 , wherein the two or more process steps associated with the two or more AIQC modules comprise:
at least two of mold inspection, ply backing removal, foreign object detection, ply backing inspection, ply templating, ply orientation inspection, ply shaping, or de-bulk leak detection.
37. The quality control method of claim 35 , further comprising:
modifying at least one of the plurality of process steps for fabricating the composite material based on at least one of the monitoring data, the quality control data, or the testing data.