The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

A method may include obtaining acquired process data regarding a plant process that is performed by a plant system. The method may further include obtaining from a process model, simulated process data regarding the plant process. The method may further include determining drift data for the process model based on a difference between the acquired process data and the simulated process data. The drift data may correspond to an amount of model drift associated with the process model. The method may further include determining whether the drift data satisfies a predetermined criterion. The method further includes determining, in response to determining that the drift data fails to satisfy the predetermined criterion, a model update for the process model.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

One or more aspects of the present invention relate to a computer-implemented method for part analytics, in particular for analyzing the quality, the machining process and preferably the engineering process, of a workpiece machined by at least one CNC machine. According to these aspects, the method may include providing a digital machine model of the CNC machine with realtime and non-realtime process data of the at least one CNC machine, the realtime and non-realtime process data being recorded during the machining process of the workpiece under consideration; and subsequently simulating the machining process under consideration by means of the digital machine model based at least partially on the recorded realtime and non-realtime process data.

Disclosed herein is a worksite automation process that involves: generating a first sequence of tasks to build the product according to a model. The process further involves causing one or more robotic devices to build the product by beginning to execute the first sequence of tasks. Further, during the execution of the first sequence of tasks, performing a buildability analysis to determine a feasibility of completing the product by executing the first sequence of tasks. Based on the analysis, determining that it is not feasible to complete the product by executing the first sequence of tasks, and in response, generating a second sequence of tasks to complete the product according to the model. Then, causing the one or more robotic devices to continue building the product by beginning to execute the second sequence of tasks.

An apparatus and method for smart portfolio optimization modeling for optimum demand response resource configuration in order to solve problems that it is difficult to collect customer objects corresponding to demand response resources and to obtain power reduction reliability and power reduction quantity of customer objects and real-time metering information about loads whose power can be reduced since conventional customer objects have a low demand response resource recognition rate and problems that power is supplied to only customer objects collected in a power exchange in power feeding according to power supply and demand situations since an apparatus for optimization modeling for a portfolio composed of optimized demand response resources is not present and thus many customer objects on which a power feed instruction is not executed are generated, decreasing a power reduction implementation rate. The apparatus includes a smart meter 100, a demand response resource network generator 200 and a smart portfolio optimization modeling control module 300 to measure metering data with respect to hourly electricity prices and hourly electricity consumption loads in real time by installing a smart meter in a customer object, to improve a customer object collection rate by as much as about 70% compared to conventional cases through supply of the smart meter free of charge and public relations and advertisement about payment of adjusted amount corresponding to saved power quantity, to generate a demand response resource network for reducing power of loads on the basis of IDs of smart meters installed in collected customer objects, thereby performing remote power monitoring and remote power control and to achieve optimization modeling for a portfolio composed of optimized demand response resources by combining customer objects, power consumption of which is to be reduced, so as to maximize power reduction on the basis of RIM net benefit maximization and energy savings maximization, thereby enhancing a power reduction implementation rate according to power supply and demand state as much as about 80% and revitalizing the negawatt market.

A method for forming a stamped component from a blank material with an industrial stamping machine during a stamping process includes measuring a plurality of parameters of the stamping process. The parameters are provided as variables of the stamping process. The method further includes analyzing, by a stamping process model, the plurality of parameters to adjust the stamping process for the blank material, defining, by the stamping process model, a control parameter of the industrial stamping machine for the blank material, and stamping the blank material with the industrial stamping machine based on the defined control parameter to form the stamped component.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

Methods, non-transitory computer readable media, and Industrial Internet-of-Things (IIoT) management apparatuses that generate event data based on an overall event comprising a plurality of dynamic multi-cloud events. Each of the events is associated with at least one of a plurality of types of IIoT resource devices or a plurality of IIoT participant devices. A current overall event hierarchy of the events is established, derived, or introduced based on one of a plurality of predefined hierarchies. A relationship between one or more socio environment and economic factors and each of the events is identified. One or more controls for each of the events are derived based on the identified relationship. Action plan data is generated for an execution in a multi-cloud environment based on the events, the derived controls, and a profiled participant role associated with one or more of the participant devices. The action plan data is distributed in the environment.

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.