Systems, methods, and media for manufacturing processes
A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.
1. 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 from the image data, the set of keypoints corresponding to position information of the tooling during processing at the first station;
determining, by a machine learning model, a final quality metric for the component, based on the set of keypoints, wherein the final quality metric is a metric associated with the component that cannot be measured until processing of the component in the multi-step manufacturing process is complete;
determining that the final quality metric is not within a threshold tolerance from a nominal final quality metric; and
based on determining that the final quality metric is not within the threshold tolerance from the nominal final quality metric, updating processing parameters of subsequent stations in the multi-step manufacturing process.
2. The manufacturing system of claim 1 , wherein the image data comprises a plurality of images, each image corresponding to a respective camera.
3. 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.
4. 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.
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 the machine learning model is a long short-term memory model.
7. 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 from the image data, the set of keypoints corresponding to position information of the tooling during processing at the first station;
determining, by a machine learning model, a final quality metric for the component, based on the set of keypoints, wherein the final quality metric is a metric associated with the component that cannot be measured until processing of the component in the multi-step manufacturing process is complete;
determining that the final quality metric is not within a threshold tolerance from a nominal final quality metric;
based on determining that the final quality metric is not within the threshold tolerance from the nominal final quality metric, inferring positional information corresponding to the component at the first station;
based on determining that the final quality metric is not within a threshold tolerance from the nominal final quality metric, generating an updated instruction set to be performed by a downstream station;
predicting, by the machine learning model, a new final quality metric for the component based on the updated instruction set, wherein the new final quality metric is a metric associated with the component that cannot be measured until processing of the component in the multi-step manufacturing process is complete; and
based on the new final quality metric, providing the updated instruction set to the downstream station.
8. The manufacturing system of claim 7 , further comprising:
determining, based on the positional information corresponding to the component, that an irreversible error is present.
9. The manufacturing system of claim 8 , further comprising:
comparing a set of points corresponding to coordinates of the component to a canonical set of points corresponding to a canonical component.
10. The manufacturing system of claim 7 , wherein generating the updated instruction set to be performed by the downstream station comprises:
applying a stochastic gradient descent to a set of actions that occurred at the first station.