Siemens Aktiengesellschaft

Josep Soler Garrido

Ingo Thon

Johannes Frank

Heiko Claussen

Steffen Lamparter

Jan-Gregor Fischer

Michal Skubacz

Gunther Birk

Maja Milicic Brandt

Igor Kogan

Andreas Macher

Norman Drews

Christian Kern

Renè Fischer

An extension device for one or more automation devices in an industrial system is provided. Industrial data processing units capable of performing data processing based on one or more artificial neural networks are provided. To enable and/or accelerate one or more computations in an industrial system, thereby simplifying integration of artificial intelligence into the industrial system, and to simplify data exchange between an extension device capable of processing data using artificial intelligence and an automation device, one or more results of the one or more computations are obtained. The results indicate one or more states of the industrial system. The one or more results are provided via a process state model shared with the automation device to monitor and/or control the industrial system.

Extension device for an automation device

A method includes determining an artificial intelligence function in an engineering framework system. An inference path is defined for generation of an AI model by a computation graph. An AI function and the inference path are converted into a processing format. The converted AI function is sent and exported to an extraction and extension module of an AI workbench module. The extended computation graph of the inference path is transmitted from the extraction and extension module to an AI framework module. The method includes communicating of a communication adapter with the processing module continuously by using a supporting communication protocol for receiving training data as input for the AI function and forwarding the training data to the AI framework module. Learned parameters of the AI model are transferred from an API interface of the AI framework module to the communication adapter for updating the AI model.

Method and system for generating an artificial intelligence model

Provided is a deployment platform for deployment of an industrial application on an edge computing device, ECD, connected to the controller of a machine tool, MT, the deployment platform including a model management component, MMC, adapted to instantiate a generic machine tool model, GMTM, stored in a memory to provide a machine instance model, MIM, of the respective machine tool, MT, on the basis of a machine tool data report, MTDR, received by the model management component, MMC, from the edge computing device, ECD, of the respective machine tool, MT, and further adapted to convert generic data requirements, gR, of a generic industrial application into machine tool specific requirements, mtsR, using the machine instance model, MIM, of the respective machine tool, MT, wherein an instantiated industrial application for the machine tool, MT, is provided by instantiating the generic industrial application by extending its configuration data with machine tool specific requirements.

Method and platform for deployment of an industrial application on an edge computing device of a machine tool

A method for deployment of a machine learning model (MLM) on a target field device is disclosed herein. The method includes automatically generating a set of source code files based on the machine learning model, wherein the set of source code files is configured to execute the machine learning model according to predetermined capabilities of the target field device; transforming the generated source code files into a model binary using a tool chain specific to the target field device; and deploying the model binary in a memory of the target field device.

Method for development and execution of a machine learning model on a field device

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Munich, Germany

Josep Soler Garrido