Systems, methods, and media for manufacturing processes
AssigneeNanotronics Imaging, Inc.
InventorsAndrew Sundstrom, Eun-Sol Kim, Damas Limoge, Vadim Pinskiy, Matthew C. Putman
Life of this patent
Estimating the term…
Abstract
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.
Claims20
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.
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.
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.
Dependencies are inferred from the claim text.