Systems, methods, and media for manufacturing processes

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.

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.