Systems, methods, and media for manufacturing processes

1. A manufacturing system, comprising:

a first station and a second station, each of the first station and the second station configured to perform at least one step in a multi-step manufacturing process for a component;

a monitoring platform configured to monitor progression of the component throughout the multi-step manufacturing process; and

a control module configured to dynamically adjust processing parameters of a step of the multi-step manufacturing process to achieve a desired final quality metric for the component, the control module configured to perform operations, comprising:

receiving image data of tooling of the first station, wherein the first station is upstream of the second station;

identifying a set of keypoints on the tooling from the image data, the set of keypoints corresponding to position information of the tooling during processing at the first station;

projecting, by a machine learning model, a final quality metric for the component, based on the set of keypoints, the final quality metric representing a metric quality of the component that cannot be measured until the multi-step manufacturing process is complete; and

assigning the component to a class of components based on a comparison between the projected final quality metric and a canonical final quality metric for the component.

2. The manufacturing system of claim 1 , wherein the operations further comprise:

determining that the final quality metric is not within a threshold tolerance from the final quality metric; and

based on the determining, updating processing parameters of the second station in the multi-step manufacturing process.

3. The manufacturing system of claim 1 , wherein the image data comprises a plurality of images, each image corresponding to a respective camera.

4. The manufacturing system of claim 1 , wherein the operations further comprise:

extracting, from the image data, a subset of images, wherein each image of the subset of images includes the tooling of the first station.

5. The manufacturing system of claim 4 , further comprises:

generating a number of points corresponding to the tooling in the image data.

6. The manufacturing system of claim 1 , wherein identifying the set of keypoints from the image data comprises:

applying blob detection to the image data to identify a location of the tooling in the image data.

7. The manufacturing system of claim 1 , wherein the machine learning model is a long short-term memory model.

8. A computer-implemented method for controlling a multi-step manufacturing process involving a first station and a second station of a manufacturing system, each of the first station and the second station configured to perform at least one step in the multi-step manufacturing process for a component, the computer-implemented method comprising:

receiving, by a computing system associated with the manufacturing system, image data of tooling of the first station, wherein the first station is upstream of the second station;

identifying, by the computing system, a set of keypoints from the image data, the set of keypoints corresponding to position information of the tooling during processing at the first station;

projecting, by a machine learning model associated with the computing system, a final quality metric for the component, based on the set of keypoints, the final quality metric representing a quality metric of the component that cannot be measured until the multi-step manufacturing process is complete; and

assigning, by the computing system, the component to a class of components based on a comparison between the projected final quality metric and a canonical final quality metric for the component.

9. The computer-implemented method of claim 8 , further comprising:

determining, by the computing system, that the final quality metric is not within a threshold tolerance from the final quality metric; and

based on the determining, updating, by the computing system, processing parameters of the second station in the multi-step manufacturing process.

10. The computer-implemented method of claim 8 , wherein the image data comprises a plurality of images, each image corresponding to a respective camera.

11. The computer-implemented method of claim 8 , further comprising:

extracting, from the image data, a subset of images, wherein each image of the subset of images includes the tooling of the first station.

12. The computer-implemented method of claim 8 , wherein identifying, by the computing system, the set of keypoints from the image data comprises:

applying blob detection to the image data to identify a location of the tooling in the image data.

13. The computer-implemented method of claim 12 , further comprising:

generating, by the computing system, a number of points corresponding to the tooling in the image data.

14. The computer-implemented method of claim 8 , wherein the machine learning model is a long short-term memory model.

15. A manufacturing system, comprising:

a first station and a second station, each of the first station and the second station configured to perform at least one step in a multi-step manufacturing process for a component;

a monitoring platform configured to monitor progression of the component throughout the multi-step manufacturing process; and

a control module configured to dynamically adjust processing parameters of a step of the multi-step manufacturing process to achieve a desired final quality metric for the component, the control module configured to perform operations, comprising:

receiving image data of tooling of the first station, wherein the first station is upstream of the second station;

identifying a set of keypoints on the tooling from the image data, the set of keypoints corresponding to position information of the tooling during processing at the first station;

projecting, by a machine learning model, a final quality metric for the component, based on the set of keypoints, the final quality metric representing a metric quality of the component that cannot be measured until the multi-step manufacturing process is complete;

assigning the component to a class of components based on a comparison between the projected final quality metric and a canonical final quality metric for the component;

based on the assigning, determining that the class assigned to the component is not an acceptable class;

based on the determining, inferring positional information corresponding to the component at the first station;

based on the determining, generating an updated instruction set to be performed by the second station;

predicting, by the machine learning model, an updated final quality metric for the component based on the updated instruction set; and

based on the updated predicted final quality metric, providing the updated instruction set to the second station.

16. The manufacturing system of claim 15 , further comprising:

determining, based on the positional information corresponding to the component, that an irreversible error is present.

17. The manufacturing system of claim 16 , further comprising:

comparing the set of keypoints corresponding to coordinates of the component to a canonical set of keypoints corresponding to a canonical component.