Systems, methods, and media for manufacturing processes

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.