Methods and apparatus for Time-Sensitive Networking Coordinated Transfer Learning in industrial settings are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to cause performance of an operation by a first machine according to a first configuration, process a performance metric of the performance of the operation by the first machine to determine whether the performance metric is within a threshold range, and in response to a determination that the performance metric is not within the threshold range, cause performance of the operation by a second machine according to a second configuration different from the first configuration.

A numerical controller acquires state data including information indicating a machining state and information indicating a selected menu item, creates a machine learning model for determining a menu item display order in menu display based on the state data, and determines a menu item display order in the menu display based on the created machine learning model and the state data.

In one aspect, a heat exchanger system is provided that includes a cooling system and a sensor configured to detect a variable of the cooling system. The heat exchanger system includes processor circuitry configured to provide the variable and a plurality of potential operating parameters of the cooling system to a machine learning model representative of the cooling system to estimate at least one of energy consumption, water usage, and chemical usage for the potential operating parameters. The processor circuitry is further configured to determine, based at least in part on the estimated at least one of energy consumption, water usage, and chemical consumption, for the potential operating parameters, an optimal operating parameter of the cooling system to satisfy a target optimization criterion.

A computer-implemented method includes operating a first autonomous system to perform a task based on executable code derived from objects in a world model of the first autonomous system. The world model objects of the first autonomous system represent an operating environment of the first autonomous system. The method includes determining an initiation trigger when the first autonomous system is to begin interaction with a second autonomous system. The second autonomous system is operated based on executable code derived from a world model that includes world model objects representing an operating environment of the second autonomous system. After the initiation trigger, the method includes sharing of the world model objects between the first and second autonomous systems. Subsequently, the method includes continuing operating the first autonomous system based on an extended world model of the first autonomous system that includes the shared world model objects of the second autonomous system.

In one aspect, a heat exchanger system is provided that includes a cooling system and a sensor configured to detect a variable of the cooling system. The heat exchanger system includes processor circuitry configured to provide the variable and a plurality of potential operating parameters of the cooling system to a machine learning model representative of the cooling system to estimate at least one of energy consumption, water usage, and chemical usage for the potential operating parameters. The processor circuitry is further configured to determine, based at least in part on the estimated at least one of energy consumption, water usage, and chemical consumption, for the potential operating parameters, an optimal operating parameter of the cooling system to satisfy a target optimization criterion.