Abstract: A computer-implemented method, a device for data processing and a computer system for controlling a control device of a conveyor system to achieve an alignment and/or a defined spacing of piece goods, wherein the control of the control device is determined by an agent acting according to Reinforcement Learning methods. An individual, local state vector of predefined dimension that is the same for all the piece goods is created for each of the piece goods and an action vector is selected from an action space according to a strategy that is the same for all piece goods for the current state vector of this piece good. These action vectors are projected onto the conveying elements, wherein conflicts are resolved. After a cycle time has elapsed, state vectors are created again for each piece good and evaluated with rewards and the strategy is adjusted.

Abstract: A quality inspection method and a quality inspection arrangement for 3D printing is provided.

Abstract: Method and apparatus for industrial scale production of a suspension for a battery, wherein an input material is processed via ball milling in a rotating chamber of a device that is effected as a continuous process with a continuously controlled addition of the input material and with a continuously controlled delivery of the processed output material, where state parameters of the input material and process parameters of the manufacturing installation are acquired as first parameters during production of the suspension, results of laboratory analyses about the state or quality of the manufactured suspension are acquired as second parameters in a learning phase during production, the first and the second parameters are used in the learning phase for training a model for predicting the state or quality via machine learning, and where the device is open-loop or closed-loop controlled outside the learning phase via the first parameters and the trained model.

Abstract: Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

Abstract: Various embodiments include a method for producing a quality test system executing a quality test model with a filter mask and a quality model to determine a quality feature of a battery cell. The system has an electrochemical impedance spectroscopic unit for capturing test data relating to the battery within a frequency range. The method includes: creating the model; and producing the system. Creating the model includes: capturing spectroscopic learning data; creating the filter mask using a first machine learning method with analysis data from part of the frequency range by consulting the filter mask and creating the model using a second machine learning method. The first and the second learning method are coupled based on the learning data. The first machine learning method creates a filter mask determining the analysis data such that the second machine learning method creates a quality model optimized with respect to maximizing the quality.

Abstract: Provided is a method for computer-aided processing of quality information of an object manufactured by stacked printed layers in an additive manufacturing system, including the steps of: receiving a quality indicator for each printed layer of the object from the manufacturing system, assigning a color out of a predefined set of colors to each quality indicator depending on the value of the quality indicator, visualizing the quality indicators of the received manufactured layers as a sequence of colored bars ordered according to the sequence of the manufactured layers the color of each bar indicating the value of the quality indicator of the respective printed layer on a graphical user interface.

Abstract: Controlling a manufacturing process by a computer-generated classification model is provided. This is combined with a reward system based on a distributed ledger and smart contracts. The classification model is trained by: Providing data entities being indicative of a property of a manufacturing of a product. Acquiring labels for each of the data entities from an agent. Determining labeling metrics based on the acquiring of the agent. Training the classification model, wherein the training set includes the data entities and their labels. Validating the trained classification model yielding a classifier score. Training a labeling score model based on the data entities, the respective labels, the labeling metrics and the classifier score. Determining a labeling score for the agent based on the labeling score model, the labels and the set of labeling metrics.

Abstract: In order to allow real-time monitoring of a tracing process during additive manufacture, a device is disclosed for the additive manufacture of a workpiece. A scanning unit (2) is designed to direct a fusing beam (3) onto a tracing spot (4). The device also has a local-resolution optical detector (5), a control unit (6) and an imaging unit (7). The imaging unit (7) is designed to image a portion (8) of the tracing surface (1) on the detector (5). The control unit (6) is designed to control the device in order to change the position of the portion (8) during manufacture.

Abstract: In order to control a technical system, a system state of the technical system is continually detected. By a trained first control model, a subsequent state of the technical system is predicted on the basis of a sensed system state. Then, a distance value is determined for a distance between the predicted subsequent state and an actually occurring system state. Furthermore, a second control model is trained by the trained first control model to predict the distance value on the basis of a sensed system state and on the basis of a control action for controlling the technical system. A subsequent state predicted by the first control model is then modified on the basis of a distance value predicted by the trained second control model. The modified subsequent state is output in order to control the technical system.

Abstract: A method for the calibration of a camera for monitoring additive manufacturing of an object in which material is applied in a plurality of layers is provided. The method includes: a) providing the camera and providing means for additive manufacturing of the object, b) capturing an image of the object being manufactured or already manufactured by the camera, c) comparing the image captured with a model of the object, d) determining a calibration function on the basis of the comparison from step c), which is intended to transform the image captured into a corrected image, wherein the corrected image of the object substantially corresponds to the model of the object, and e) calibrating the camera by the calibration function. Also provided is a computer program comprising commands which, when executed by a computer, cause the computer to execute the steps of the method as well as a related apparatus.

Abstract: Provided is an apparatus and method for cooperative controlling wind turbines of a wind farm, wherein the wind farm includes at least one pair of turbines aligned along a common axis approximately parallel to a current wind direction and having an upstream turbine and a downstream turbine. The method includes the steps of: a) providing a data driven model trained with a machine learning method and stored in a database, b) determining a decision parameter for controlling at least one of the upstream turbine and the downstream turbine by feeding the data driven model with the current power production of the upstream turbine which returns a prediction value indicating whether the downstream turbine will be affected by wake, and/or the temporal evolvement of the current power production of the upstream turbine; c) based on the decision parameter, determining control parameters for the upstream turbine and/or the downstream turbine.

Abstract: Provided is a method for computer-aided processing of quality information of an object manufactured by stacked printed layers in an additive manufacturing system, including the steps of: receiving a quality indicator for each printed layer of the object from the manufacturing system, assigning a color out of a predefined set of colors to each quality indicator depending on the value of the quality indicator, visualizing the quality indicators of the received manufactured layers as a sequence of colored bars ordered according to the sequence of the manufactured layers the color of each bar indicating the value of the quality indicator of the respective printed layer on a graphical user interface.

Abstract: For controlling a target system, e.g. a gas or wind turbine or another technical system, a pool of control policies is provided. The pool of control policies comprising a plurality of control policies and weights for weighting each of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. With that, the target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system are received. Furthermore, the weights are adjusted on the basis of the received performance data to improve the performance of the controlled target system. With that, the plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.

Abstract: An apparatus and method for monitoring a quality of an object of a 3D-print job series of identical objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, including: determining a layer quality indicator of a currently printed layer of an object, comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements, and generating a warning signal, if the layer quality indicator of the currently printed layer has a value equal or lower than the lower quality limit is provided.

Abstract: In order to control a technical system, a system state of the technical system is continually detected. By a trained first control model, a subsequent state of the technical system is predicted on the basis of a sensed system state. Then, a distance value is determined for a distance between the predicted subsequent state and an actually occurring system state. Furthermore, a second control model is trained by the trained first control model to predict the distance value on the basis of a sensed system state and on the basis of a control action for controlling the technical system. A subsequent state predicted by the first control model is then modified on the basis of a distance value predicted by the trained second control model. The modified subsequent state is output in order to control the technical system.

Abstract: The present invention provides an enhanced setup of a 3D-printing device, especially to a laser powder bed fusion 3D-printing device. It is for this purpose, that data relating to previously printed products are stored in a database. When a new product is to be printed, the features of the new product are matched with features of previously printed products stored in the database. Accordingly, a suggestion for setting-up the 3D-printing device based on corresponding previously printed products and their setup parameters can be automatically determined and applied to the 3D-printing device.

Abstract: The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation (26) emitted by a tissue sample (24) of the tissue is sensed (10) by means of a radiation sensor (22), the radiation sensor (22) providing a sensor signal (28) in accordance with the sensed electromagnetic radiation, and the sensor signal (28) is evaluated (12) by means of an evaluation unit (30) in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit (30) is a self-learning evaluation unit (30) that is initially trained (14) by means of training data sets (32) on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets (32) each comprising a training sensor signal with an associated training tissue type.

Abstract: The present invention provides an enhanced setup of a 3D-printing device, especially to a laser powder bed fusion 3D-printing device. It is for this purpose, that data relating to previously printed products are stored in a database. When a new product is to be printed, the features of the new product are matched with features of previously printed products stored in the database. Accordingly, a suggestion for setting-up the 3D-printing device based on corresponding previously printed products and their setup parameters can be automatically determined and applied to the 3D-printing device.

Abstract: For controlling a target system, e.g. a gas or wind turbine or another technical system, a pool of control policies is provided. The pool of control policies comprising a plurality of control policies and weights for weighting each of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. With that, the target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system are received. Furthermore, the weights are adjusted on the basis of the received performance data to improve the performance of the controlled target system. With that, the plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.

Abstract: For controlling a target system, such as a gas or wind turbine or another technical system, a pool of control policies is used. The pool of control policies including a plurality of control policies and weights for weighting each control policy of the plurality of control policies are received. The plurality of control policies is weighted by the weights to provide a weighted aggregated control policy. The target system is controlled using the weighted aggregated control policy, and performance data relating to a performance of the controlled target system is received. The weights are adjusted based on the received performance data to improve the performance of the controlled target system. The plurality of control policies is reweighted by the adjusted weights to adjust the weighted aggregated control policy.