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
• adjusting, by the computing system, process parameters of at least the second station based on the projected final quality metric.

A manufacturing system, comprising:

• a plurality of stations, each station configured to perform at least one step in a multi-step manufacturing process for a component;
• 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 a first station of the plurality of stations;
• 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;
• determining that the final quality metric is outside of a range of acceptable values;
• based on the determining, generating an updated instruction set to be performed by a second station downstream of the first station;
• predicting, by the machine learning model, an updated final quality metric for the component based on the updated instruction set;
• and
• determining that the updated final quality metric is within the range of acceptable values;
• and
• responsive to determining that the updated final quality metric is within the range of acceptable values, providing the updated instruction set to the second station.