公开(公告)号：US20220284153A1

公开(公告)日：2022-09-08

申请号：US17632481

申请日：2019-08-28

Applicant: SIEMENS AKTIENGESELLSCHAFT ,  Siemens Industry Software Inc.

Inventor： Stefan Gavranovic ,  Suraj Ravi Musuvathy ,  Dirk Hartmann ,  Peter Nanson ,  Richard Collins ,  Hiren Dedhia

IPC: G06F30/20 ,  G06F30/10

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.

公开(公告)号：US20220253576A1

公开(公告)日：2022-08-11

申请号：US17629967

申请日：2019-08-27

Applicant: Siemens Industry Software Inc.

Inventor： Suraj Ravi Musuvathy ,  David Madeley ,  Lucia Mirabella ,  Stefan Gavranovic ,  Dirk Hartmann

IPC: G06F30/23 ,  G06F30/17 ,  B29C64/393 ,  B33Y50/02

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.

公开(公告)号：US20190325270A1

公开(公告)日：2019-10-24

申请号：US16388958

申请日：2019-04-19

Applicant: Siemens Aktiengesellschaft

Inventor： Stefan Gavranovic ,  Dirk Hartmann

IPC: G06K9/62 ,  G06N3/08 ,  G06F17/50

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.

公开(公告)号：US20190137974A1

公开(公告)日：2019-05-09

申请号：US16307352

申请日：2017-05-26

Applicant: Siemens Aktiengesellschaft

Inventor： Utz Wever ,  David Vitoux ,  Stefan Gavranovic ,  Dirk Hartmann

IPC: G05B19/4099 ,  G06F17/50 ,  B33Y50/02

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.

公开(公告)号：US11551037B2

公开(公告)日：2023-01-10

申请号：US16388958

申请日：2019-04-19

Applicant: Siemens Aktiengesellschaft

Inventor： Stefan Gavranovic ,  Dirk Hartmann

IPC: G06V10/75 ,  G06K9/62 ,  G06N3/08 ,  G06F30/20

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.

公开(公告)号：US20200326195A1

公开(公告)日：2020-10-15

申请号：US16841112

申请日：2020-04-06

Applicant: Siemens Aktiengesellschaft

Inventor： Stefan Gavranovic ,  Sylvia Glas ,  Veronika Heinrich ,  Harald Held ,  Christine Zeller

IPC: G01C21/34 ,  G08G1/07 ,  G08G1/015 ,  G08G1/01 ,  G06N3/08

Abstract: Provided is a method and an apparatus for computer-implemented traffic control of motor vehicles in a predetermined area, in which, at predetermined intervals, respectively: values of traffic parameters are ascertained in the predetermined area by way of data capture, wherein at least some of the traffic parameters relate to the motor vehicles traveling in the predetermined area and the traffic parameters of a respective motor vehicle include its current position, direction of travel and speed; a set of optimized traffic control actions, which include the adjustment of variable traffic signaling devices, is determined by an optimization using a learned data-driven model and the optimized traffic control actions are carried out, wherein the optimization takes account of predicted air quality values and the data-driven model is learned by training data from a simulation.