A programmable device (D) arranged in a production environment, to assist an operator (O) in performing manual assembly operations carried out by the operator (O), particularly during assembly operations performed on pieces (P) transported by pallets (5) in a production line (1). The device (D) comprises an assembly means usable by the operator (O), a lighting device (4) for lighting a work area in which the operator (O) works, a sensor (6) configured to detect the position of the assembly means, an input device (10) usable by the operator, and an electronic control system (8) configured to memorize a learning sequence including a sequence of manual assembly operations.

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.

A method for employing a plurality of automated machines to deposit composite material includes a first tool located in a first station and a second tool located in a second station. The first station and the second station are located on a production line. The first station includes at least one automated machine of the plurality of automated machines and the second station includes at least two automated machines of the plurality of automated machines. At least one of the automated machines is movable from the second station to the first station. The method includes monitoring machine capacity and workload requirements of the plurality of automated machines. The method further includes determining an efficiency threshold based upon the machine capacity and workload requirements. The method further includes reallocating at least one of the automated machines from the second station to the first station once the efficiency threshold is met.

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.

An autonomous system used for a production process includes a device configured to manipulate workpieces according to production process tasks. A device controller generates world model of the autonomous system to include data objects representing respective physical objects in the production process, such as workspace, workpieces, and the device. Semantic markers attached to the data objects include information related to a skill to accomplish a task objective. Semantic markers may be activated or deactivated depending on whether the physical object is currently available for a task performance. The device is controlled to perform tasks guided by the semantic markers while relying on an anticipation function with reasoning operations based on types of physical objects, types of skills, and configuration of the data objects.

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

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 machine learning device, which performs a task using a plurality of industrial machines and learns task sharing for the plurality of industrial machines, includes a state variable observation unit which observes state variables of the plurality of industrial machines; and a learning unit which learns task sharing for the plurality of industrial machines, on the basis of the state variables observed by the state variable observation unit.

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.

Provided are a product performance prediction modeling method and apparatus, a product performance prediction method, a product performance prediction system, a computer device, and a storage medium. The product performance prediction modeling method includes: acquiring first sample data, wherein the first sample data includes device outlier data generated in a process of manufacturing a product by a device; acquiring a production line configuration simulation parameter of a production line where the device is located, and product information of the product manufactured by the production line; selecting a simulation model to perform simulation test on the performance of the product, so as to obtain product performance simulation data; and inputting the device outlier data, the production line configuration simulation parameter, the product information and the product performance simulation data into a machine learning model to perform machine learning training, so as to obtain a product performance prediction model. The foregoing product performance prediction modeling method and apparatus, product performance prediction method, product performance prediction system, computer device and storage medium can accurately predict product performances during device exception.