A system and method for data collection and frequency analysis with self-organization functionality includes analyzing with a processor a plurality of sensor inputs, sampling with the processor data received from at least one of the plurality of sensor inputs at a first frequency, and self-organizing with the processor a selection operation of the plurality of sensor inputs.

The present invention increases the productivity of an entire factory, even of the type that produces different types of products in different quantities using transport vehicles, by performing transport operation control. This transport operation control device is provided with: a spatial distribution measuring unit which measures the spatial distribution of products-in-process being transported by the transport vehicles; and an operation schedule calculation unit which, on the basis of the measured spatial distribution of the group of partly-finished products, calculates an operation schedule that specifies both a route and a frequency of a transport operation to be carried out by each transport vehicle of the transport vehicles, wherein the operation schedule calculation unit determines the timing with which to update the operation schedule, on the basis of changes in a productivity index that is determined from the measured spatial distribution of the products-in-process.

A data acquisition system for dielectric analysis measurements, including a sensor interface configured to connect to one or more sensors located within an active machining zone of an industrial manufacturing machine, a module processor coupled to the sensor interface and configured to receive measurement values from one or more sensors connected to the sensor interface, a cloud interface coupled to the module processor, and a machine interface coupled to the module processor. The measurement values indicate physical properties of workpieces processed in the active machining zone of an industrial manufacturing machine. The cloud interface is configured to connect to cloud-based resources, and the machine interface is configured to connect to a controller of the industrial manufacturing machine. The module processor is configured to transmit the received measurement values from the one or more dielectric sensors to cloud-based resources via the cloud interface and to transmit manufacturing control signals to the controller of the industrial manufacturing machine via the machine interface, the manufacturing control signals being based on parameters received from cloud-based resources via the cloud interface.

A method and system for assembling a modular structure from components configured to be fitted together to create the modular structure in accordance with a construction plan. Each component includes: a communication interface; and an output interface configured to generate sensory signals. The components communicate, via the communication interface of each component, with a controller to establish an availability of the components for the modular structure. A target component is determined at the controller from the components based on the construction plan and the availability of the components. The target component is a starting component used for initiating creation of the modular structure in accordance with the construction plan. A first sensory signal indicating the target component to a user is generated by the output interface of the target component.

A manufacturing system 10 is configured to include a driving device 4 configured to drive a manufacturing machine 5 in a step pertaining to product manufacture in a predetermined production management system, the driving device 4 driving the manufacturing machine 5 in the step in response to predetermined information obtained related to a state of the step.

A control system for a central plant having subplants including devices operating to serve energy loads of a building. The system includes a high level optimization module that performs high level optimization of thermal loads subject to constraints to generate subplant load allocations. The control system includes a low level optimization module that performs low level optimization of the subplant load allocations to determine operating states for the devices. The control system includes a constraint modifier that modifies the constraints for the high level optimization module based on equipment schedules. The control system also includes a binary optimization modifier including a pruner module that receives the minimum off schedule to determine adjusted branches and a seeder module that receives the minimum on schedule to determine a starting node for use in binary optimization performed by the low optimization module.

Reliability with which a control device at a transmission destination can receive data is improved. In a communication system in which a plurality of control devices for factory automation are connected to a network, a communication protocol among the control devices includes a publish/subscribe type. Each of the control devices includes: a data storage unit in which control data is stored; a publisher unit that transmits data including a state of the control device of the publisher unit itself to other control devices than the control device; and a subscriber unit that receives data from each of the other control devices. When a state of each of the other control devices that is received by the subscriber unit satisfies a reception allowing condition, the publisher unit transmits the control data of the control device of the publisher unit itself.

Various embodiments of the present technology relate to digital twins of devices and assemblies. More specifically, some embodiments relate to the orchestration of digital twin models for representing industrial systems based on characteristics of digital twins. In an embodiment, a method of operating an orchestration engine in an industrial automation environment comprises identifying a targeted outcome for modeling the industrial automation environment, configuring a digital twin environment corresponding to the industrial automation environment based at least on the targeted outcome, and executing a process associated with the industrial automation environment using the digital twin environment.

In a process of transfer of production control in proximity to production site for enabling decentralized manufacturing, production control data and routing data are retrieved from a centralized system. The retrieved production control data and the routing data are stored within a connector installed at a proximity to a plant floor. An operation associated with the production control data of a purchase order is matched with the resource published in the connector. Upon determining the match between the operation and the resource, the resource is dynamically allocated to perform the operation. The control of manufacturing process of the plant floor is provided to the connector to realize de-centralized manufacturing.

A systematic approach to constructing process plans for hybrid manufacturing is provided. The process plans include arbitrary combinations of AM and SM processes. Unlike the suboptimal conventional practice, the sequence of AM and SM modalities is not fixed beforehand. Rather, all potentially viable process plans to fabricate a desired target part from arbitrary alternating sequences of pre-defined AM and SM modalities are explored in a systematic fashion. Once the state space of all process plans has been enumerated in terms of a partially ordered set of states, advanced artificial intelligence (AI) planning techniques are utilized to rapidly explore the state space, eliminate invalid process plans, for instance, process plans that make no physical sense, and optimize among the valid process plans using a cost function, for instance, manufacturing time and material or process costs.