A system, apparatus and method for determining operational configuration of asset are provided. The method includes receiving a set of operating parameters associated with the asset, identifying data associated with the asset based on the received set of operating parameters, configuring a second digital twin of the asset based on the data identified from first knowledge database, simulating a behavior of the asset based on the configured second digital twin in a simulation environment, and determining an operational configuration associated with the variant of the asset based on results of the simulation.

An industrial automation system may include an automation device and a control system communicatively coupled to the automation device. The control system may include a first module of a number of modules, such that the first module may receive an indication of a target variable associated with the industrial automation device. The first module may then receive parameters associated with the target variable, identify a portion of data points associated with controlling the target variable with respect to the parameters, generate a model of each data point of the portion over time with respect to the parameters based on the data points, determine functions associated with the model. The functions represent one or more relationships between the each data point of the portion with respect to controlling the target variable. The first module may then adjust one or more operations of the automation device based on the functions.

A digital twin management system manages a virtual model that represents an actual physical system in a virtual space on a real-time basis. To generate an integrated virtual model by adding a second virtual model to a first virtual model, a processor of the digital twin system extracts multiple parts that can be used in common in the first virtual model and the second virtual model, generates multiple integrated virtual models that are candidates of an integrated virtual model by changing the extracted parts that can be used in common, calculates an evaluation of each of the generated integrated virtual models, and outputs configuration information regarding each of the integrated virtual model candidates and an evaluation of the integrated virtual model candidate in association with each other.

In one aspect, there is provided a computer-implemented method that incudes obtaining physical sensor measurements of a physical robotic operating environment and obtaining a virtual representation of the robotic operating environment. The method further includes generating a user interface presentation including a first view of the virtual representation based virtual sensor parameters, and a second view of the physical robotic operating environment based on the physical sensor measurements. The method further includes receiving an update to values of the virtual sensor parameters, and updating, in the user interface presentation, the first view of the virtual representation based on the updated values of the virtual sensor parameters.

One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.

One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.

The disclosure relates to digital models of a production apparatus. The digital models can generate simulations of production sequences of the production apparatus, and a controller can access the simulation to improve operations of the production apparatus. The digital model uses data of a locating system to create the simulation. The locating system monitors carriers for transporting components. The controller can compare parameters of the simulation results with corresponding parameters of earlier simulation results and/or actually obtained parameters of earlier production sequences, which can be stored in a model library. The disclosure further relates to corresponding production control methods.

A building time for building an additively-manufactured object is calculated on the basis of the inter-pass time and the welding pass time and is compared with a preset upper limit value, and welding conditions in a depositing plan are repeatedly modified until the building time is equal to or less than the upper limit value. Alternatively, corrections are repeatedly performed until the shape difference between a building shape of built-up object shape data relating to the additively-manufactured object created on the basis of the inter-pass time and the inter-pass temperature, and a building shape of three-dimensional shape data, is smaller than a near net value.

A method includes receiving a plurality of operations in a sequence recipe. The plurality of operations are associated with processing a plurality of substrates in a substrate processing system. The method further includes identifying a plurality of completion times corresponding to the plurality of operations. Each completion time of the plurality of completion times corresponds to completion of a respective operation of the plurality of operations. The method further includes simulating the plurality of operations by setting a virtual time axis to each of the plurality of completion times to generate a schedule for the sequence recipe. The method further includes causing, based on the schedule, the plurality of substrates to be processed or performance of a corrective action.

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.