Siemens Aktiengesellschaft

Birgit Obst

Dirk Hartmann

Florian Ulli Wolfgang Schnös

Utz Wever

Sven Tauchmann

Hans-Georg Köpken

Lucia Mirabella

David Bitterolf

Stefan Boschert

Moritz Allmaras

Daniel Regulin

Chethan Ravi B R

Michael Jaentsch

Tobias Kamps

Bony Mathew

Erik Olof Johannes Wannerberg

Florian Ulli Wolfgang SCHNÖS

Mathew Bony

Chetan Ravi B R

Rishith Ellath Meethal

A method for operating a numerical controlled machine comprising receiving a sequence of control commands which, when executed by a numerical controlled machine, cause the numerical controlled machine to machine a workpiece to obtain a predetermined workpiece geometry, wherein the sequence of control commands includes while machining the workpiece based on the received sequence of control commands measuring a value of a first interaction parameter for a first position of the tool, comparing a measured value of the first interaction parameter for the first position of the tool with the simulated value of the first interaction parameter for the first position of the tool, and determining an adapted value of the second interaction parameter for a following position of the tool based on a result of the comparison.

Online multi-force-adaption during machining

In order to be able to take into account machining configurations more flexibly, a method for optimizing numerically controlled machining of a workpiece includes ascertaining geometric interaction data. A relationship between a force to be expected and a configuration parameter of the machining is determined on the basis of the interaction data. The force is calculated during the machining on the basis of the relationship and a current value of the at least one configuration parameter. The machining is adapted depending on the calculated force.

Computer-aided optimization of numerically controlled machining of a workpiece

Provided is a method for the computerized control of an end element of a machine tool. The method includes a method step of sensing a plurality of optical markers in a work environment of the machine tool by means of an optical measuring system. The method includes a method step of determining a first relative pose between the end element and a workpiece on the basis of the plurality of sensed optical markers. The method includes a method step of determining a first correction value on the basis of a comparison of the first relative pose with a reference pose. The method includes a method step of controlling the end element for machining the workpiece taking the first correction value into consideration.

Method for controlling an end element of a machine tool, and a machine tool

A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

System state prediction

A system, a control unit, and a method for controlling operation of a technical system are provided. The technical system includes a plurality of sensors. The method includes receiving first sensor data from a first sensor of the plurality of sensors. The method includes detecting a first sensor anomaly based on failure of the first sensor to generate the first sensor data. The failure of the first sensor includes generation of anomalous first sensor data. The method also includes validating the first sensor anomaly based on a comparison between the first sensor data and a virtual first sensor data. Thereafter, a control command is generated to the technical system by replacing the virtual first sensor data in lieu of the first sensor data when the first sensor anomaly is validated.

Controlling and maintaining operational status during component failures

A method using machine learned, scenario based control heuristics including: providing a simulation model for predicting a system state vector of the dynamical system in time based on a current scenario parameter vector and a control vector; using a Model Predictive Control, MPC, algorithm to provide the control vector during a simulation of the dynamical system using the simulation model for different scenario parameter vectors and initial system state vectors; calculating a scenario parameter vector and initial system state vector a resulting optimal control value by the MPC algorithm; generating machine learned control heuristics approximating the relationship between the corresponding scenario parameter vector and the initial system state vector for the resulting optimal control value using a machine learning algorithm; and using the generated machine learned control heuristics to control the complex dynamical system modelled by the simulation model.

Method and system for providing an optimized control of a complex dynamical system

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.

Method for computer-aided control of an automation system

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Munich, Germany

Birgit Obst