A machining simulation apparatus performs a machining simulation in a machine tool by controlling a relative position between a tool model and a material model to perform a machining of the material model with the tool model. The machining simulation is performed with an input of at least one of a position command and a position detection value. The position command and the position detection value are obtained from the machine tool as log data corresponding to a time. A result of the machining simulation is displayed.

A production line scheduling method, adapted to a plurality of jobs passing a bottleneck station having at least one manufacturing machine, the jobs respectively correspond to a plurality of job conditions, and the method includes: performing a plurality of times of a schedule simulation algorithm on the jobs to sequentially establish a plurality of schedule simulation trees, and obtaining a job schedule and a simulated finishing period of each job based on the schedule simulation trees; and calculating a plurality of expected feeding times of each job at a plurality of stations including the bottleneck station, each schedule simulation tree includes at least one scheduling route, and each scheduling route is generated from one schedule simulation algorithm, the schedule simulation algorithm includes: performing a node expansion step based on at least one node expansion condition and the job conditions to obtain the scheduling route.

An IoT environment including at least one source configured to provide IoT data associated with at least one asset of at least one industrial plant, at least one user device; and a cloud computing system communicatively coupled to the at least one source and the at least one user device. The cloud computing system configures a digital twin corresponding to at least one of the asset and a production process associated with the industrial plant based on the IoT data received from the source. Further, at least one data package is generated based on the configured digital twin upon receiving a request for accessing the digital twin from the user device. The at least one data package is transmitted to the user device, for enabling the user device to perform one or more simulations based on the data package.

Embodiments of the disclosure provide for intelligent model integration within a process simulation system. Some embodiments receive data associated with the operation of a plant, determine, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model, train the intelligent model using the at least one qualifying dataset, and deploy the trained intelligent model for use.

A user interface system for a process plant includes a graphic display editor to configure a process graphic display having a graphic display element representative of a process plant element of the process plant. The process graphic display is specified via configuration information set forth in a declarative language. A graphics rendering engine generates a depiction of the process graphic display during runtime based on commands derived from the configuration information set forth in the declarative language. The configuration information for the process graphic display may be stored as an object, which, for instance, may include first and second portions to define a graphical parameter and identify a data source, respectively. The graphical parameter may be directed to defining a graphical depiction of the process plant element and, to this end, may be set forth in a formal in accordance with the declarative language. The data source may specify a location or path for data indicative of on-line operation of the process plant element to be displayed via the graphical depiction.

A system and method monitor and/or diagnose the operation of a production line of an industrial plant which is controlled by an automation system. The system includes a remote data processing server, which is installed outside of the industrial plant. The remote data processing server is configured to receive a digital input signal reflecting at least one control input signal and a digital output signal reflecting a second operational state, to determine at least first and second modeled states corresponding to the at least first and second operational states, respectively, by inputting the digital input and the digital output signals to a digital observer model of the production line and the automation system and by processing the digital observer model, and to forward the first and second modeled states to an output interface from where they can be accessed by modeling and/or diagnosing modules.

An automated control of a system having a plurality of cooperating components involving controlled elements and sensors uses a simulator configured to simulate operation of the components. The simulator stores data representing states of the components and modifies the states over time in accordance with simulated operation of the system. An input module receives data from at least the sensors and updates in the simulator the data representing states of the components. An output module reads from the simulator the data representing states of the components and generates at least controlled element control signals for the controlled elements of the components. The simulator contains a virtual state machine representing the system, and automation of the system is achieved without state machine logic representing the system within the input module and the output module.

Example systems and methods may allow for use of a generic robot trajectory format to control a robotic process within a workcell. One example method includes receiving a digital representation of at least one digital robot actor, including at least one robot definition corresponding to the at least one digital robot actor and at least one sequence of robot operations for the at least one digital robot actor, determining a mapping between the at least one digital robot actor and at least one corresponding physical robot actor within a physical workcell, generating at least one robot-language-specific sequence of executable instructions for the at least one physical robot actor, and transmitting the at least one robot-language specific sequence of executable instructions to the at least one physical robot actor to execute in order to perform the at least sequence of robot operations within the physical workcell.

An arrangement for testing a control unit via a test environment, having a computer-based test management tool, wherein the test management tool is configured for model-based development and/or management of at least one test plan implemented as a data structure in order to test the control unit, and the test plan has at least one test and a start condition for initiating execution of the test plan; a computer-based test execution control tool, wherein the test execution control tool is configured to initiate execution of the test plan on the test environment when the start condition is met; and a computer-based database, wherein the database is configured to store the test plan implemented as a data structure and is also configured for shared, common access to the test plan by the test management tool and the test execution control tool.

An industrial development hub (IDH) supports industrial development and testing capabilities that are offered as a cloud-based service. The IDH comprises an enhanced storage platform and associated design tools that serve as a repository on which customers can store control project code, device configurations, and other digital aspects of an industrial automation project. The IDH system can facilitate discovery and management of digital content associated with control systems, and can be used for system backup and restore, code conversion, and version management. The IDH also supports simulation-based design and testing tools.