Abstract: For detecting a cyber-attack on a machine controller, a concurrent simulation of the machine is run in a secured access domain. From the machine controller actual control data are transmitted to the machine and resulting monitoring data are transmitted to a monitoring device. Furthermore, sensor data of the machine are transmitted to the concurrent simulation on a first secured transmission path. Based on the sensor data, the concurrent simulation simulates an operational behavior of the machine, thus inferring simulated monitoring data. The simulated monitoring data are then compared with the resulting monitoring, and an alarm signal is triggered depending on the comparison.

Abstract: The invention relates to a friction surface clutch (10) for use in motor vehicles, in particular for switching an air compressor or the like, wherein the friction surface clutch comprises a first tapered element (11) having a first friction surface (12) and a second tapered element (13) having a second friction surface (14), wherein the friction surface clutch has an actuating device (15) having an actuating element (16) for force-locked connection and disconnection of the tapered elements, wherein the tapered elements are in force-locked connection in an unactuated operating state of the friction surface clutch, wherein the actuating device comprises a pressure element (21) coupled to the actuating element and a lever device (22) interacting with the first tapered element, wherein the pressure element can be engaged with the lever device in an actuated operating state of the friction surface clutch so that the force-locked connection can be disconnected, wherein the first tapered element has a guide part, in

Abstract: 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.

Abstract: Provided is a method for determining a physical shape having a predefined physical target property that includes calculating a sensitivity landscape on the basis of a shape data record for the physical shape with the aid of a calculation device. The calculation device is a machine-taught artificial intelligence device. The shape data record identifies locations at or on the physical shape. For a plurality of these locations, the sensitivity landscape respectively indicates how the target property of the physical shape changes if the physical shape changes in the region of the location. Furthermore, the shape data record for the physical shape to be determined is changed on the basis of the sensitivity landscape in such a manner that the predefined physical target property is improved.

Abstract: 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.

Abstract: 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.

Abstract: In order to separate excess material from an additively manufactured component, spatially resolved structural data on the component are received. On the basis of the structural data, a process for emptying material from the component is simulated, wherein a sequence of emptying poses of the component is determined. For an associated emptying pose: the component is moved into the associated emptying pose in accordance with the simulated emptying process, movement of material is detected by sensors, as a result of a detection of a decrease in the movement of material, a trigger signal is generated, and a movement of the component into a subsequent emptying pose is initiated by the trigger signal, the trigger signal being considered higher priority than the simulated emptying process.

Abstract: A computing system may include a geometry access engine configured to access geometries associated with a topology optimization process, including an original geometry that represents a design space upon which the topology optimization process applies to as well as a topology optimized geometry that represents an output of the topology optimization process performed for the original geometry. The system may also include geometry processing engine configured to generate a final geometry from the topology optimized geometry, including by conforming the topology optimized geometry to the original geometry at portions of the topology optimized geometry that correspond to fixed regions of the original geometry as well as smoothing the topology optimized geometry at portions that correspond to non-fixed regions of the original geometry.

Abstract: To separate excess material, the component moved by a movement device that is controlled by movement data, and a fill level of the component with material is measured. A process for emptying material from the component simulated for each different initial fill level with material, wherein movement data, which specify a simulated movement of the component, and a simulated fill level progression resulting from the simulated movement are assigned to the associated initial fill level. In addition, a corresponding initial fill level is selected in accordance with the measured fill level, and the movement device is controlled by movement data which are assigned to the selected initial fill level. The fill level is measured and compared to a simulated fill level progression assigned to the selected initial fill level. The steps of selecting a corresponding initial fill level (SAFG) and controlling the movement device (BV) are carried out.

Abstract: A computing system may include a design access engine and a design processing engine. The design access engine may be configured to access an object design to be constructed through additive manufacturing. The design processing engine may be configured to represent the object design as a combination of coarse geometric elements and high-resolution lattice elements and process the object design based on both the coarse geometric elements and the high-resolution lattice elements. Processing of the object design may include generation of lattice infills, lattice simulations, or a combination of both.

Abstract: A computer-implemented method and apparatus for generating a design for a technical system or a product is provided. Depending on a set of first parameters, specifying physical properties, and second parameters, specifying perceptible properties of the technical system or product, a design is generated for the technical system or product. A performance indicator that evaluates a physical performance of the generated design is obtained. The generated design of the technical system or product is presented to a user and perception data in response to the presentation of the generated design are measured by a perception capturing unit and a perception evaluation indicator is deduced from the measured perception data. An optimized design is determined by iteratively optimizing the performance indicator and/or the perception evaluation indicator by an optimization algorithm. The method and apparatus enable an autonomous closed design loop taking human perception into account.

Abstract: 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.

Abstract: 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.

Abstract: Provided is a dispersion stabilizer for suspension polymerization comprising an aqueous emulsion containing a dispersant (A), a dispersoid (B), a graft polymer (C), and an aqueous medium, wherein the dispersant (A) contains a surfactant, the dispersoid (B) contains a polymer having an ethylenically unsaturated monomer unit, the graft polymer (C) is obtained by graft polymerization of the ethylenically unsaturated monomer with the dispersant (A), a mass ratio [A/(A+B+C)] is 0.001 or more and less than 0.18, a mass ratio [C/(A+B+C)] is 0 or more and less than 0.04, and the total content of the dispersant (A), the dispersoid (B), and the graft polymer (C) is 35 to 70 mass %. A vinyl polymer to be obtained by suspension polymerization of a vinyl compound using the dispersion stabilizer has good plasticizer absorption. Further, the number of fish-eyes that occur when the vinyl polymer is formed is small, and the hue deterioration is also suppressed.

Abstract: A method (1) for optimizing a production process for a component (20, 32) that is to be manufactured by additive manufacturing by means of simulation (2) of the production process (50) includes: a) ascertaining a position of the component (20, 32) in a production space that has been optimized according to a process optimization criterion (7); b) calculating displacements and/or stresses in the component (20, 32) that can be caused by the production process (50); c) ascertaining supporting structures (31) that counteract the displacements and/or stresses that have been optimized according to the process optimization criterion (7); and d) ascertaining at least a portion of the design of the component (20, 32) that has been optimized according to a component optimization criterion (8).

Abstract: Provided is a method for determining a physical shape having a predefined physical target property that includes calculating a sensitivity landscape on the basis of a shape data record for the physical shape with the aid of a calculation device. The calculation device is a machine-taught artificial intelligence device. The shape data record identifies locations at or on the physical shape. For a plurality of these locations, the sensitivity landscape respectively indicates how the target property of the physical shape changes if the physical shape changes in the region of the location. Furthermore, the shape data record for the physical shape to be determined is changed on the basis of the sensitivity landscape in such a manner that the predefined physical target property is improved.

Abstract: 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.

Abstract: Design data are input for an object to be additively manufactured and to be optimised in terms of a physical optimisation target is provided. A volumetric model of the object is initialised with a material distribution according to the design data, the volumetric model having a plurality of volume elements. A respective local target property relating to the optimisation target is then determined for volume elements of the volumetric model, based on the material distribution. According to embodiments of the invention, each volume element is checked to determine whether the volume element is supported in terms of additive manufacturing. Based on this, the target property of this volume element is modified in such a way that it approaches the target property if it is supported and/or moves away from the optimisation target if it is not supported.

Abstract: A method for performing an optimized control of a complex dynamical system 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 simulat

Abstract: A method includes the following steps: observing a first state vector including state variables in a physical system A; determining a first prediction vector based on the first state vector, with a data driven model for system A; determining a second prediction vector based on the first state vector, with a physics based model for system A; training a prediction fusion operator to determine a third prediction vector based on the first and second prediction vectors; validating the prediction fusion operator on the third prediction vector and another first state vector, the other first state vector concerning the same time as the third prediction vector.