Abstract: A system includes a station information system, a portable vision system, and a quality monitoring system. The station information system includes a station computing device configured to provide a notification related to a manufacturing operation performed on a component. The portable vision system includes a quality check module configured to include a station task module configured to execute a quality check task based on an image. The quality monitoring system includes a quality monitoring computing device configured to request the portable vision system to execute the quality check task based on a trigger message from the station information system and to provide a task data message related to the quality check task executed by the portable vision system to the station information system. The station computing device is configured to provide the notification based on the task data message from quality monitoring system via the user interface device.

Abstract: A method includes obtaining state information from one or more sensors of a digital twin. The method includes determining an action at the first routing control location based on the state information, where the action includes one of a pallet merging operation and a pallet splitting operation, and determining a consequence state based on the action. The method includes calculating a transient production value based on the consequence state and a transient objective function, calculating a steady state production value based on the consequence state and a steady state objective function, and selectively adjusting one or more reinforcement parameters of the reinforcement learning system based on the transient production value and the steady state production value.

Abstract: A method includes obtaining state information from one or more sensors of a digital twin. The method includes determining an action at the first routing control location based on the state information, where the action includes one of a pallet merging operation and a pallet splitting operation, and determining a consequence state based on the action. The method includes calculating a transient production value based on the consequence state and a transient objective function, calculating a steady state production value based on the consequence state and a steady state objective function, and selectively adjusting one or more reinforcement parameters of the reinforcement learning system based on the transient production value and the steady state production value.

Abstract: A method includes obtaining sensor data from a plurality of sensors disposed at a plurality of routing control locations of an environment, where the sensor data is indicative of a number of a plurality of pallets at the plurality of routing control locations. The method includes calculating a plurality of difference values based on the sensor data, calculating a transient production value based on the sensor data and a transient objective function, and calculating a steady state production value based on the sensor data and a steady state objective function. The method includes generating a state vector based on the plurality of difference values, the transient production value, and the steady state production value, and defining a set of routes for a set of pallets from among the plurality of pallets based on the state vector and a digital twin of the environment.

Abstract: A method includes defining a sensor layout of a digital twin based on one or more sensor parameters and one or more routing control locations of the digital twin. The method includes simulating a manufacturing routine of a plurality of pallets and a plurality of workstations based on one or more pallet parameters associated with the plurality of pallets and one or more workstation parameters associated with the plurality of workstations and calculating, for each routing control location from among the one or more routing control locations, a transient production value and a steady state production value based on the manufacturing routine. The method includes iteratively adjusting the sensor layout of the digital twin until each transient production value is less than or equal to a threshold transient production value and each steady state production value is less than or equal to a threshold steady state production value.