Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based recommendation engine configured to, an industrial programming environment, generate a recommendation to add a component to control logic based on an existing portion of the control logic. A notification component is configured to surface the recommendation in the programming environment. A programming component is configured to, in the programming environment, add the component to the control logic. A configuration component is configured to configure the component based at least in part on the existing portion of the control logic.

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.

In a method for analyzing a mechatronic system which has one or more mechatronic components, structure data is provided. The structure data is representative of a predefined structure for a network. The structure has a plurality of layers and a respective layer is representative in each case of a technical domain of the mechatronic system. A model in the form of a multilayer network is generated depending on a multiplicity of input data relating to the mechatronic system and to the predefined structure. The multilayer network comprises a multiplicity of nodes and a plurality of connections in each case between two nodes. Each node of the plurality of nodes is assigned to one of the plurality of layers. The mechatronic system is analyzed depending on the multilayer network.

Multi-target libraries, projects, and activities for robotic process automation (RPA) are disclosed. Some embodiments multiple target platforms can be handled in the same project. The target platform(s) can be specified at the automation and/or activity level in order to provide the supported functionality for each. This may also allow previously built automations to be applied to new target frameworks without starting from scratch.

An information processing device keeps a plurality of sets of designing information each on a combination of a plurality of plant devices that execute an operation a plant and at least one control device that executes control on each of the plant devices, keeps operational past records with respect to each combination of a plurality of plant devices and a control device, extracts combinations of a plurality of plant devices and a control device that meet a designing condition on the plant based on the designing information, and outputs the extracted combinations of a plurality of plant devices and a control device based on the operational past records.

A method is implemented on a computer device to configure a control device for closed-loop control of a sub-system in a plant for production of food products. The computer device obtains definition data, DD, which indicates a task performed by the sub-system in the production of food products and equipment in the sub-system for performing the task; derives a candidate process model, CPM, of the sub-system based on DD; obtains measurement data, MD, generated by the sub-system when operated in accordance with a test sequence, TS; estimates constant parameter(s) of differential equation(s) in the CPM based on TS and MD; defines a final process model, APM, for the sub-system based on the differential equation(s) and the constant parameter(s); and operates a tuning algorithm on APM to determine control parameter(s) of the control device for closed-loop control.

Systems, methods, and devices are disclosed herein for the integrated design and deployment of process control logic and power control logic in the context of industrial automation environments. In an implementation, a programmable logic controller executes power control logic to control a power control module on the network of the electrical subsystem of an industrial automation process. The power control module relays electrical device information to the programmable logic controller which issues power control commands according to the power control logic. The power and process control logic executed by the programmable logic controller are designed in an integrated design environment which includes representations of process control logic for controlling manufacturing devices of the industrial automation process and of power control logic for controlling electrical devices in the industrial automation process.