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 process model management system includes a processor and a storage device, the storage device being configured to store actual data of tasks at a manufacturing site and master data including design information of the tasks. The processor is configured to generate an actual process model based on the actual data, generate a master process model based on the master data, generate a synthetic process model by synthesizing the actual process model and the master process model, when the actual data is changed, detect a difference of the actual process model due to the change of the actual data, when the master data is changed, detect a difference of the master process model due to the change of the master data, detect a difference of the synthetic process model due to the change of the actual data or the master data, and output a notification based on the detected difference.

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.

A method for controlling operation of an electrolyzer plant includes using a first model to determine first operating points for a first period of time; using a second model to simulate operation of the electrolyzer plant for the first operating points for a second period of time that is shorter than and comprised in the first period of time, the second model being a model having higher prediction accuracy for the operation of the electrolyzer plant than the first model, and determining whether the simulated operation meets a predetermined requirement. Upon determining that the simulated operation does not meet the predetermined requirement, adjusting one or more parameters and/or one or more boundary conditions of the first model, and upon determining that the simulated operation meets the predetermined requirement, setting the first operating points as target operating points for the predetermined second period of time.

A computer apparatus runs a hydraulic model using real-time or near-real-time data from an Automated or Advanced Metering Infrastructure (AMI), to improve model accuracy, particularly by obtaining more accurate, higher-resolution water demand values for service nodes in the model. Improving the accuracy of water demand calculation for the service nodes in the model stems from an improved technique that more accurately determines which consumption points in the water distribution system should be associated with each service node and from the use of real-time or near-real-time consumption data. The computer apparatus uses the water demand values to improve the accuracy and resolution of its water flow and pressure estimates. In turn, the improved flow and pressure estimation provides for more accurate control, e.g., pumping or valve control, flushing control or scheduling, leak detection, step testing, etc.

A manufacturing device inputs design information including three-dimensional structure data, generates a manufacturing process flow, and displays the manufacturing process flow on a screen for a user to check, modify, and confirm the flow based on design information and setting information. A process method includes a first process method of a direct modeling method having an FIB method and a second process method of a semiconductor manufacturing process method which is a non-FIB method. The manufacturing device generates a plurality of manufacturing process flows by a combination of cases where each of the process methods is applied to each of the regions of the three-dimensional data. The manufacturing process flow includes a process device, the process method, a control parameter value, a process time, and a total process time for each of process steps. An output unit outputs a manufacturing process flow having, for example, the shortest total process time.

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 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.

A method for computer-aided control of an automation system is provided by use of a digital simulation model which simulates the automation system and which is specified by a number parameters comprising a number of configuration parameters) describing the configuration of the automation system and a number of state parameters describing the operational state of the automation system. Simulated operation runs of the automation system based on the simulation model can be performed with the aid of a computer, where a simulation run predicts a number of performance parameters of the automation system.