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 product. The monitoring platform is configured to monitor progression of the product 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 product.
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 product;
a monitoring platform configured to monitor progression of the product throughout the multi-step manufacturing process; and
a computing system configured to adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the product, the computing system configured to perform operations, comprising:
receiving, by the computing system from the monitoring platform, an input associated with the product at a step of the multi-step manufacturing process, wherein the input comprises an image of the product at the step of the multi-step manufacturing process;
generating, by a state autoencoder of the computing system, a state encoding for the product based on the input;
determining, by a deep learning model of the computing system, based on the state encoding and the image of the product that a final quality metric for the product is not within a range of acceptable values; and
based on the determining, adjusting by the computing system, a control logic for at least a following station, wherein the adjusting comprises generating, by the deep learning model, a corrective action to be performed by the following station.
2. The manufacturing system of claim 1 , wherein the final quality metric cannot be measured until processing of the product is complete.
3. The manufacturing system of claim 1 , wherein adjusting, by the computing system, the control logic for at least the following station, comprises:
identifying the corrective action to be performed by the following station; and
projecting the final quality metric based on the corrective action and the state encoding.
4. The manufacturing system of claim 1 , wherein the operations further comprise:
determining, by the computing system, whether an irrecoverable failure is present based on the input.
5. The manufacturing system of claim 4 , wherein the input comprises an image and wherein the computing system determines that the irrecoverable failure is present using a convolutional neural network.
6. The manufacturing system of claim 1 , wherein adjusting by the computing system, the control logic for at least the following station, comprises:
adjusting a further control logic for a further following station.
7. The manufacturing system of claim 1 , wherein each of the one or more stations correspond to a layer deposition in a 3D printing process.
8. A multi-step manufacturing method, comprising:
receiving, by a computing system from a monitoring platform of a manufacturing system, an image of a product at a station of one or more stations, each station configured to perform a step of a multi-step manufacturing process;
generating, by a state autoencoder of the computing system, a state encoding for the product based on the image of the product;
determining, by a deep learning model of the computing system, based on the state encoding and the image of the product that a final quality metric of the product is not within a range of acceptable values; and
based on the determining, adjusting by the computing system, a control logic for at least a following station, wherein the adjusting comprises generating, by the deep learning model, a corrective action to be performed by the following station.
9. The multi-step manufacturing method of claim 8 , wherein the final quality metric cannot be measured until processing of the product is complete.
10. The multi-step manufacturing method of claim 8 , wherein adjusting, by the computing system, the control logic for at least the following station, comprises:
identifying the corrective action to be performed by the following station; and
projecting the final quality metric based on the corrective action and the state encoding.
11. The multi-step manufacturing method of claim 8 , further comprising:
determining, by the computing system, whether an irrecoverable failure is present based on the image.
12. The multi-step manufacturing method of claim 11 , wherein the computing system determines that an irrecoverable failure is present using a convolutional neural network.
13. The multi-step manufacturing method of claim 8 , wherein adjusting by the computing system, the control logic for at least the following station, comprises:
adjusting a further control logic for a further following station.
14. The multi-step manufacturing method of claim 8 , wherein each of the one or more stations correspond to a layer deposition in a 3D printing process.
15. A three-dimensional (3D) printing system, comprising:
a processing station configured to deposit a plurality of layers to form a product;
a monitoring platform configured to monitor progression of the product throughout a deposition process; and
a computing system configured to adjust processing parameters for each layer of the plurality of layers to achieve a desired final quality metric for the product, the computing system configured to perform operations, comprising:
receiving, by the computing system from the monitoring platform, an image of the product after a layer has been deposited;
generating, by a state autoencoder of the computing system, a state encoding for the product based on the image of the product;
determining, by a deep learning model of the computing system, based on the state encoding and the image of the product that a final quality metric for the product is not within a range of acceptable values; and
based on the determining, adjusting, by the computing system, a control logic for depositing at least a following layer of the plurality of layers, wherein the adjusting comprises generating, by the deep learning model, a corrective action to be performed during deposition of the following layer.
16. The system of claim 15 , wherein the final quality metric cannot be measured until processing of the product is complete.
17. The system of claim 15 , wherein adjusting, by the computing system, the control logic for depositing at least the following layer, comprises:
identifying the corrective action to be performed during deposition of the following layer; and
projecting the final quality metric based on the corrective action and the state encoding.
18. The system of claim 15 , further comprising:
determining, by the computing system, whether an irrecoverable failure is present based on the image.
19. The system of claim 18 , wherein the computing system determines that the irrecoverable failure is present using a convolutional neural network.
20. The system of claim 15 , wherein adjusting by the computing system, the control logic for depositing at least the following layer, comprises:
adjusting a further control logic for a further following layer.