Embodiments of this present disclosure may include a system that includes a first network device. The first network device may perform an operation according to a device configuration file. The system may also include a second network device that directly communicatively couples to the first network device through a peer-to-peer (P-P) communication network. The second network device may include a backup file of the device configuration file. The second network device may transmit the backup file of the device configuration file to the first network device in response to detecting that the first network device is lacking the device configuration file.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Disclosed is a method of determining at least one tolerance band limit value for a technical variable under test. The method includes obtaining the at least one tolerance band limit value from sample tolerance band limit values of different samples, wherein the samples comprise values of the technical variable under test of the associated sample, wherein obtaining the at least one tolerance band limit value comprises using a location measure of a distribution according to which the sample tolerance band limit values are distributed, wherein the technical variable under test is distributed according to an underlying extreme value distribution function, wherein each of the sample tolerance band limit values is calculable using a sample-specific conditional probability distribution function which is a function of sample values of the sample, and wherein the technical variable relates to a physical characteristic of a product that is producible in an industrial mass production process.

Provided are a device and a method which enable easy analysis and evaluation of work efficiency of a worker without burdensome tasks and easy determination of a worker's skill level by comparing the worker's work efficiency status with that of another worker or a past record of the same worker, wherein analytical information is produced by estimating the worker's joint positions based on a video; acquiring time series data on joint positions; determining work efficiency based on the time series data; acquiring a target range (part of a work process with low work efficiency); output an image of the target range overlaid on a graph of the time series data; and output a posture image of the worker overlaid on the video. The analytical information may include information on a working activity to be analyzed and information on a chosen model working activity with high work efficiency.

Various embodiments of the teachings herein include a method for building a graph-based industrial flow model. The method may comprise: importing a set of entities based on an industrial flow including a user-defined entity and a system-defined entity; querying the model for the user-defined entity, and if found, obtaining a connection of the user-defined entity, otherwise generating a new implementation layer; querying the model for an implementation layer of the user-defined entity, and if found, obtaining a connection of the implementation layer of the user-defined entity, otherwise generating a new implementation layer; querying the model for the system-defined entity, and if the system-defined entity is found, obtaining a connection of the system-defined entity, otherwise generating a new implementation layer; and iteratively performing the above queries until a final graph-based model for the industrial flow results.

Provided are an operation control device and program capable of properly synthesizing drive signals of two lineages. An operation control device for controlling an operation of a manufacturing system including an industrial machine using drive signals of at least two lineages, the device comprising a first drive signal output unit for outputting a first drive signal as a drive signal of a first lineage, a second drive signal output unit for outputting a second drive signal as a drive signal of a second lineage, a synthesized drive signal generation unit for generating a synthesized drive signal by synthesizing the first drive signal and the second drive signal, and an operation control unit for controlling an operation of the manufacturing system on the basis of the first drive signal, the second drive signal, and the synthesized drive signal.

An NC device as a numerical control device controls an additive manufacturing apparatus for producing an object by layering, on a workpiece, a material melted by being irradiated with a beam. The NC device includes: a feature quantity extracting unit that extracts, from image data, a feature quantity for determining a welding state that is a state where a molten material is added to the workpiece; and a process map creating unit that creates a process map in which a shape of the object and a layering condition are associated with each other. The layering condition is selected from among a plurality of layering conditions on the basis of a result of determination of the welding state, and includes at least one of beam intensity and a supply amount of a material.

Two or more computational services are defined that each represent a respective different manufacturing capability used to partially create a target part model. A common space shared among the computational services is defined to reference the target part model and manufacturing primitives corresponding to each capability. The computational services are queried to construct a logical representation of the planning space based on intersections among the primitives. One or more process plans are formed using the different manufacturing capabilities to manufacture the part.

The controller communicable with a second controller include circuitry configured to: execute a processing to operate a first machine in collaboration with a second machine controlled by the second controller in a real space; modify the processing in response to determining that, instead of controlling the second machine, the second controller controls a virtual second machine that simulates operations of the second machine in a virtual space; and execute the modified processing to operate the first machine in the real space in collaboration with the virtual second machine that operates in the virtual space.

A method includes defining a manufacturing transformation for a mobile workpiece based on mobile workpiece state information of the mobile workpiece, where the manufacturing transformation is an automated operation to be performed on the mobile workpiece. The method includes selecting a set of one or more manufacturing systems from among the one or more manufacturing systems for the mobile workpiece based on the manufacturing transformation and manufacturing system state information associated with the one or more manufacturing systems, where the manufacturing system state information provides data related to at least one of availability, capability, and constraints of the of the one or more manufacturing systems. The method includes defining manufacturing process steps of the manufacturing transformation based on the set of one or more manufacturing systems. The method includes defining a location for performing the manufacturing process steps on the mobile workpiece.