Abstract: Various embodiments of the teachings herein include a method for controlling a battery production system. An example includes: measuring production parameters with a plurality of sensors; determining a quality value of a battery cell with the measurement values; calculating a dependency of the quality value on the measurement values with a computing unit; calculating a dependency of the quality value on changed production parameters by performing a machine learning method; determining a controllable process parameter with an improved quality value using a parameter optimization for the machine learning method including a Bayesian optimization, wherein measurement values with associated quality values are included in the optimization as reference points; and using the controllable process parameter with an improved quality value in operation of the battery production system.

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: To configure a machine controller by an action execution tree, predefined action patterns are read in. A multiplicity of action execution trees for a machine to be controlled is also generated. For a respectively generated action execution tree, a performance for controlling the machine based on the respective action execution tree is determined. The predefined action patterns are also sought in the respective action execution tree. An action pattern found in the respective action execution tree is then replaced at least in part by a reference to the predefined action pattern. A tree size of the thus modified action execution tree is furthermore determined. Based on the generated action execution trees, a numerical optimization method is then used to determine an action execution tree that is optimized with regard to better performance and smaller tree size, and this is output in order to configure the machine controller.

Abstract: Computer-implemented method and control apparatus for controlling a machine by executing program steps which verifiably execute predefined control tasks is provided, including manipulating data based on input data received from the machine and resulting in reference data by data programming steps structured according to a declarative programming paradigm in a data processing unit, and performing control program steps verifying the reference data with respect to explicit conditions, without any further manipulation of the reference data in the control program steps in a control processing unit, and providing instructions resulting from the performed control program steps to the machine for execution of the instructions by the machine.

Abstract: A computer implemented method for automatically generating a behavior tree program for controlling a machine includes the steps of: transmitting a sequence of machine commands input by a user from a user interface to a controller, receiving supervision data in the user interface from the controller while the machine commands are executed in the controller controlling the machine, observing and copying the machine commands and supervision data transmitted between the controller and the user interface, storing the machine commands and the supervision data in a logging unit, generating a behavior tree program derived from the stored machine commands and the supervision data by statistical inference, and sending the generated behavior tree program to the controller unit to control the machine.

Abstract: A computer implemented method for automatically generating a behavior tree program for controlling a machine includes the steps of: transmitting a sequence of machine commands input by a user from a user interface to a controller, receiving supervision data in the user interface from the controller while the machine commands are executed in the controller controlling the machine, observing and copying the machine commands and supervision data transmitted between the controller and the user interface, storing the machine commands and the supervision data in a logging unit, generating a behavior tree program derived from the stored machine commands and the supervision data by statistical inference, and sending the generated behavior tree program to the controller unit to control the machine.

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: 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: 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: 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 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: A computer implemented method and a system are disclosed for predicting the remaining numbers of error or the remaining time to the end of test mainly applicable in software projects. In at least one embodiment, the prediction can be improved by using the test progress of the current project and the gradient derived from at least one former project having similar characteristics as the current project e.g. release developments for determining parameters for a reliability growth model. The method and the system can be implemented by adapted software and hardware commercially available off the shelf.