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:
one or more stations, each 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 a first station of the one or more stations;
determining, by a first machine learning model, a final quality metric for the component, based on the image data;
determining that the final quality metric is outside an acceptable range of the desired final quality metric;
based on the determining, generating an updated instruction set to be performed by at least one of the first station or a downstream station to achieve the desired final quality metric;
predicting, by a second machine learning model, an updated final quality metric for the component based on the updated instruction set; and
based on the updated final quality metric, providing the updated instruction set to at least one of a first station controller associated with the first station or a second station controller associated with the downstream station.
2. The manufacturing system of claim 1 , wherein the second machine learning model is trained with a training set comprising a synthetic set of data.
3. The manufacturing system of claim 1 , wherein the final quality metric cannot be measured until processing of the component is complete.
4. The manufacturing system of claim 1 , further comprising:
determining, based on positional information of the component, that an irreversible error is not present.
5. The manufacturing system of claim 1 , further comprising:
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.
6. The manufacturing system of claim 5 , wherein the set of keypoints are used by the first machine learning model to determine the final quality metric.
7. The manufacturing system of claim 5 , further comprising:
comparing the set of keypoints corresponding to coordinates of the component to a canonical set of keypoints corresponding to a canonical component.
8. A method for dynamically adjusting processing parameters of a step of a multi-step manufacturing process to achieve a desired final quality metric for a component undergoing the multi-step manufacturing process, comprising:
receiving, by a computing system, image data of tooling of a first station of a plurality of stations involved in the multi-step manufacturing process;
determining, by a first machine learning model of the computing system, a final quality metric for the component, based on the image data, the final quality metric representing a metric associated with the component that cannot be measured until the multi-step manufacturing process is complete;
determining, by the computing system, that the final quality metric is outside an acceptable range of the desired final quality metric;
based on the determining an updated instruction set to be performed by at least one of the first station or a downstream station to achieve the desired final quality metric;
predicting, by a second machine learning model of the computing system, an updated final quality metric for the component based on the updated instruction set; and
based on the updated final quality metric, providing, by the computing system, the updated instruction set to at least one of a first station controller associated with the first station or a second station controller associated with the downstream station.
9. The method of claim 8 , wherein the second machine learning model is trained with a training set comprising a synthetic set of data.
10. The method of claim 8 , wherein the final quality metric cannot be measured until processing of the component is complete.
11. The method of claim 8 , further comprising:
determining, based on positional information of the component, that an irreversible error is not present.
12. The method of claim 8 , further comprising:
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.
13. The method of claim 12 , wherein the set of keypoints are used by the first machine learning model to determine the final quality metric.
14. The method of claim 12 , further comprising:
comparing the set of keypoints corresponding to coordinates of the component to a canonical set of keypoints corresponding to a canonical component.
15. A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by a computing system, causes a processor to perform operations for dynamically adjusting processing parameters of a step of a multi-step manufacturing process to achieve a desired final quality metric for a component undergoing the multi-step manufacturing process, comprising:
receiving, by the computing system, image data of tooling of a first station of one or more stations involved in the multi-step manufacturing process;
determining, by a first machine learning model of the computing system, a final quality metric for the component, based on the image data;
determining, by the computing system, that the final quality metric is outside an acceptable range of the desired final quality metric;
based on the determining, generating an updated instruction set to be performed by at least one of the first station or a downstream station to achieve the desired final quality metric;
predicting, by a second machine learning model of the computing system, an updated final quality metric for the component based on the updated instruction set; and
based on the updated final quality metric, providing, by the computing system, the updated instruction set to at least one of a first station controller associated with the first station or a second station controller associated with the downstream station.
16. The non-transitory computer readable medium of claim 15 , wherein the second machine learning model is trained with a training set comprising a synthetic set of data.
17. The non-transitory computer readable medium of claim 15 , wherein the final quality metric cannot be measured until processing of the component is complete.
18. The non-transitory computer readable medium of claim 15 , further comprising:
determining, based on positional information of the component, that an irreversible error is not present.
19. The non-transitory computer readable medium of claim 15 , further comprising:
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.
20. The non-transitory computer readable medium of claim 19 , wherein the set of keypoints are used by the first machine learning model to determine the final quality metric.