Systems, devices, articles, and methods for the partition of a plurality of items. A system including at least one processor, a frame, an end-effector coupled to the frame, a conveyor, a buffer area proximate to the end-effector and overlying at least a part of the conveyor. Coupled to the at least one processor is the end-effector, the conveyor, and a storage device storing processor-executable instructions which cause the at least one processor to direct the end-effector partition a plurality of items into two or more predefined parts per a defined partition for the plurality of items. Items in the two or more parts are placed in the buffer area, transferred to the conveyor, and may be transferred to a plurality of containers. A method of operation of a system including at least one processor and a robot substantially as described and illustrated herein.

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, the first sample data including 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 relating to a location of the device, 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, 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, to obtain a product performance prediction model.

A method includes identifying sets of part data associated with substrate processing equipment. Each of the sets of part data includes corresponding part values and a corresponding part identifier. Each of the sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment. The method further includes generating sets of aggregated data. Each of the sets of aggregated data includes a corresponding set of part data of the sets of part data and a corresponding set of additional non-part data of sets of non-part data. The method further includes causing, based on the sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.

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.

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.

In an industrial automation environment, a three-tier architecture is used to offload human-machine-interaction (HMI) automation tasks to local mobile devices and then the cloud, to take advantage of distributed computing and processing resources and to add new features to the HMI panel system. A scheduling algorithm based on the characteristics of the HMI tasks distributes these tasks intelligently among the local HMI panel, mobile devices and the cloud, to best utilize the merits of each tier.

A process control system includes: one or a plurality of field devices configured to be placed in a plant; and a control apparatus configured to perform at least one of input and output on the field device to control the plant; and a change trend calculation device configured to calculate a change trend in time-series data including an observed value at each point in time of the field device.

Systems, devices, articles, and methods for the partition of a plurality of items. A system including at least one processor, a frame, an end-effector coupled to the frame, a conveyor, a buffer area proximate to the end-effector and overlying at least a part of the conveyor. Coupled to the at least one processor is the end-effector, the conveyor, and a storage device storing processor-executable instructions which cause the at least one processor to direct the end-effector partition a plurality of items into two or more predefined parts per a defined partition for the plurality of items. Items in the two or more parts are placed in the buffer area, transferred to the conveyor, and may be transferred to a plurality of containers. A method of operation of a system including at least one processor and a robot substantially as described and illustrated herein.

On the basis of received first region information indicating a first region, which is a region designated for an acquired picked-up image of a plurality of target objects, and second region information indicating a second region different from the first region, a robot control device causes a robot to grip the target object for which the second region not overlapping the first region of another of the target objects is designated and does not cause the robot to grip the target object, the second region for which overlaps the first region of the other target object.

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.