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公开(公告)号：US11567470B2
公开(公告)日：2023-01-31
申请号：US17310300
申请日：2019-12-17
发明人： Dirk Hartmann , David Bitterolf , Hans-Georg Köpken , Birgit Obst , Florian Ulli Wolfgang Schnös , Sven Tauchmann
IPC分类号： G05B19/402 , G05B19/31
摘要： 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.
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公开(公告)号：US20220382265A1
公开(公告)日：2022-12-01
申请号：US17775839
申请日：2020-11-18
发明人： Dirk Hartmann , Michael Jaentsch , Tobias Kamps , Birgit Obst , Daniel Regulin , Florian Ulli Wolfgang Schnös , Sven Tauchmann
IPC分类号： G05B19/418
摘要： 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.
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公开(公告)号：US20230185257A1
公开(公告)日：2023-06-15
申请号：US18015831
申请日：2021-07-13
CPC分类号： G05B13/048 , G05B13/027
摘要： A machine controller, geometry data and measured physical data of a machine is provided. The geometry data and the physical data are input to a machine learning module and to a simulation module of the machine controller. By the input data, the simulation module generates first values of a first physical property of a component of the machine on a discretized grid. Furthermore, an evaluator is provided for evaluating a physical compatibility of the first values with second values of a second physical property of the component, and for generating a residual quantifying the compatibility. The evaluator evaluates the compatibility of the first values with output data of the machine learning module and generates a resulting residual. Moreover, the machine learning module is trained to minimize the resulting residual, thus configuring the machine controller for controlling the machine by the output data of the trained machine learning module.
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公开(公告)号：US20140316747A1
公开(公告)日：2014-10-23
申请号：US13865777
申请日：2013-04-18
发明人： Birgit Obst , Tim Schenk , Roland Rosen , Stefan Boschert , Veronika Brandstetter , Jorg Nieveler , Moritz Allmaras , Thomas Gruenewald , George Lo
IPC分类号： G06F17/50
CPC分类号： G06F17/5004 , G06F17/5009
摘要： Method for generating boundary conditions for at least one model for the simulation of at least one civil infrastructure, said method comprising the process steps: (a) mapping of spatially distributed installations connected to the at least one civil infrastructure onto a data structure; (b) typification of the spatially distributed installations; and (c) determination of boundary conditions for the at least one model by means of the spatially distributed installations that have been typified.
摘要翻译： 用于为至少一个民用基础设施的模拟产生用于至少一个模型的边界条件的方法，所述方法包括以下过程步骤：（a）将连接到所述至少一个民用基础设施的空间分布式设备映射到数据结构; （b）空间分布式装置的典型化; 和（c）借助于以典型的空间分布式安装确定至少一个模型的边界条件。
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公开(公告)号：US11347996B2
公开(公告)日：2022-05-31
申请号：US16106153
申请日：2018-08-21
发明人： Moritz Allmaras , Birgit Obst
摘要： 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.
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公开(公告)号：US20190138886A1
公开(公告)日：2019-05-09
申请号：US16106153
申请日：2018-08-21
发明人： Moritz Allmaras , Birgit Obst
摘要： 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.
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公开(公告)号：US20190031204A1
公开(公告)日：2019-01-31
申请号：US15963240
申请日：2018-04-26
摘要： 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 simulation model.
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公开(公告)号：US10953891B2
公开(公告)日：2021-03-23
申请号：US15963240
申请日：2018-04-26
摘要： 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.
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公开(公告)号：US10571872B2
公开(公告)日：2020-02-25
申请号：US15336850
申请日：2016-10-28
发明人： Stefan Boschert , Lucia Mirabella , Birgit Obst , Utz Wever
摘要： 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.
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