公开(公告)号：US11694415B2

公开(公告)日：2023-07-04

申请号：US17083153

申请日：2020-10-28

Applicant: AUTODESK, INC.

Inventor： Ran Zhang ,  Morgan Fabian ,  Ebot Etchu Ndip-Agbor ,  Lee Morris Taylor

IPC: G06T19/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/20

CPC classification number: G06T19/20 ,  G06N3/08 ,  G06T3/40 ,  G06T17/205 ,  G06T2219/2016 ,  G06T2219/2021

Abstract: In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.

公开(公告)号：US11681971B2

公开(公告)日：2023-06-20

申请号：US17098282

申请日：2020-11-13

Applicant: AUTODESK, INC.

Inventor： David Benjamin ,  Damon Lau ,  James Stoddart ,  Lorenzo Villaggi ,  Rui Wang ,  Lindsey Wikstrom

IPC: G06Q10/06 ,  G06Q10/04 ,  G06F30/13 ,  G06Q10/10 ,  G06F16/901 ,  G06Q50/16 ,  G06F30/20 ,  G06F30/28 ,  G06F30/18 ,  G06Q50/08 ,  G06T17/20 ,  G06F111/04 ,  G06F111/02 ,  G06F111/10 ,  G06F119/02 ,  G06Q10/0639 ,  G06Q10/0633 ,  G06Q10/047 ,  G06Q10/101 ,  G06Q10/0637 ,  G06Q10/067

CPC classification number: G06Q10/06393 ,  G06F16/9024 ,  G06F30/13 ,  G06F30/18 ,  G06F30/20 ,  G06F30/28 ,  G06Q10/047 ,  G06Q10/067 ,  G06Q10/0633 ,  G06Q10/06375 ,  G06Q10/06395 ,  G06Q10/101 ,  G06Q50/08 ,  G06Q50/163 ,  G06Q50/165 ,  G06F2111/02 ,  G06F2111/04 ,  G06F2111/10 ,  G06F2119/02 ,  G06T17/20

Abstract: A computer-implemented method for computationally determining ventilation efficiency when generating a building design comprises: generating a first three-dimensional (3D) mesh based on a first 3D building model; performing a first fluid dynamic computer simulation based on the first 3D mesh and first environmental data associated with the first 3D building model to generate a first output data set; and computing, based on the first output data set, a first value for a ventilation performance metric that is associated with the first 3D building model.

公开(公告)号：US20230177224A1

公开(公告)日：2023-06-08

申请号：US17707886

申请日：2022-03-29

Applicant: Autodesk, Inc.

Inventor： Benjamin McKittrick Weiss ,  Nigel Jed Wesley Morris ,  Adrian Adam Thomas Butscher ,  Jesus Rodriguez

IPC: G06F30/12 ,  G06T19/20 ,  G06T15/06

CPC classification number: G06F30/12 ,  G06T19/20 ,  G06T15/06 ,  G06F2119/18

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design with feature thickness control, include: a three-dimensional modeling program configured to provide voxelized thinning based distance to medial surface processing that measures thicknesses in a three-dimensional model, and/or ramped scaling based thickness constraint application during shape and/or topology generation. The three-dimensional modeling program can be an architecture, engineering and/or construction program (e.g., building information management program), a product design and/or manufacturing program (e.g., a CAM program), and/or a media and/or entertainment production program (e.g., an animation production program).

公开(公告)号：US11654565B2

公开(公告)日：2023-05-23

申请号：US16940288

申请日：2020-07-27

Applicant: AUTODESK, INC.

Inventor： Hui Li ,  Evan Patrick Atherton ,  Erin Bradner ,  Nicholas Cote ,  Heather Kerrick

IPC: G06F17/00 ,  B25J9/16 ,  G06N20/00 ,  G05B19/418 ,  G06N3/04 ,  G06N3/08 ,  G06F30/20 ,  G06T17/00

CPC classification number: B25J9/1671 ,  B25J9/161 ,  B25J9/163 ,  B25J9/1605 ,  G05B19/41885 ,  G06F30/20 ,  G06N3/0445 ,  G06N3/08 ,  G06N20/00 ,  G06T17/00 ,  G05B2219/32017 ,  G05B2219/35353

Abstract: One embodiment of the present invention sets forth a technique for controlling the execution of a physical process. The technique includes receiving, as input to a machine learning model that is configured to adapt a simulation of the physical process executing in a virtual environment to a physical world, simulated output for controlling how the physical process performs a task in the virtual environment and real-world data collected from the physical process performing the task in the physical world. The technique also includes performing, by the machine learning model, one or more operations on the simulated output and the real-world data to generate augmented output. The technique further includes transmitting the augmented output to the physical process to control how the physical process performs the task in the physical world.

公开(公告)号：US20230145217A1

公开(公告)日：2023-05-11

申请号：US18077948

申请日：2022-12-08

Applicant: Autodesk, Inc.

Inventor： Andrew John Harris ,  Konara Mudiyanselage Kosala Bandara ,  Dagmara Lilianna Szkurlat ,  Adrian Adam Thomas Butscher ,  Anthony Christopher Kipkirui Yegon Ruto

IPC: G06F30/23 ,  G06F30/10 ,  B29C64/393 ,  B33Y50/02

CPC classification number: G06F30/23 ,  G06F30/10 ,  B29C64/393 ,  B33Y50/02 ,  G06F2111/04

Abstract: Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes, A method includes obtaining, by a computer aided design program, a design space for a modeled object, one or more design criteria for the modeled object, one or more in-use load cases, and a critical fatigue crack length for a material from which A physical structure will be manufactured; iteratively modifying a generatively designed three dimensional shape of the modeled object in the design space in accordance with the critical fatigue crack length for the material, wherein the iteratively modifying comprises enforcing a design criterion that limits a minimum thickness of the generatively designed three dimensional shape, the minimum thickness being based on the critical fatigue crack length for the material.

公开(公告)号：US20230141215A1

公开(公告)日：2023-05-11

申请号：US18086516

申请日：2022-12-21

Applicant: Autodesk, Inc.

Inventor： Andreas Linas Bastian ,  Gregory David Meess

IPC: B29C64/393 ,  B33Y10/00 ,  B33Y50/02 ,  B29C64/106 ,  B29C64/40

CPC classification number: B29C64/393 ,  B29C64/40 ,  B29C64/106 ,  B33Y10/00 ,  B33Y50/02 ,  B33Y30/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for working with three-dimensional object models for printing. One of the methods includes determining a plurality of infill structures in a slice of an object; and determining a path for the tool-head to create the plurality of infill structures including: determining a first portion of the path for deposition of a first infill structure during a first time period; determining a second portion of the path for deposition of one or more second infill structures that are not adjacent to the first infill structure during a second time period; and determining a third portion of the path for deposition of a third infill structure that is adjacent to the first infill structure, wherein the second time period is determined to allow the first infill structure to cool before deposition of the third infill structure.

公开(公告)号：US11607806B2

公开(公告)日：2023-03-21

申请号：US16892216

申请日：2020-06-03

Applicant: AUTODESK, INC.

Inventor： Michael Haley ,  Erin Bradner ,  Pantelis Katsiaris

IPC: B25J9/16

Abstract: A model generator implements a data-driven approach to generating a robot model that describes one or more physical properties of a robot. The model generator generates a set of basis functions that generically describes a range of physical properties of a wide range of systems. The model generator then generates a set of coefficients corresponding to the set of basis functions based on one or more commands issued to the robot, one or more corresponding end effector positions implemented by the robot, and a sparsity constraint. The model generator generates the robot model by combining the set of basis functions with the set of coefficients. In doing so, the model generator disables specific basis functions that do not describe physical properties associated with the robot. The robot model can subsequently be used within a robot controller to generate commands for controlling the robot.

公开(公告)号：US20230065286A1

公开(公告)日：2023-03-02

申请号：US18046819

申请日：2022-10-14

Applicant: Autodesk, Inc.

Inventor： Patrick Liam Colm Tierney ,  Abhijit Oak

IPC: G06F30/13 ,  G06Q50/08

Abstract: A method, apparatus, system, and computer program product generates construction metrics. Building means, methods, and limitations of construction for one or more companies are gathered in a computer. A digital building information model (BIM) is acquired. Fabrication and construction parameters are extracted from the BIM. Construction metrics (for the BIM) are generated by combining the building means, methods, and limitations with the extracted fabrication and construction parameters. The construction metrics are then visualized in a modeling application and/or used to output construction process documentation.

公开(公告)号：US20230060989A1

公开(公告)日：2023-03-02

申请号：US18047607

申请日：2022-10-18

Applicant: AUTODESK, INC.

Inventor： James STODDART ,  David BENJAMIN ,  Danil NAGY ,  Damon LAU

IPC: G06F30/15 ,  B62D65/00

Abstract: In various embodiments, a generative design application generates and evaluates automotive designs. In operation, the generative design application computes a first set of metric values based on a set of metrics associated with design goal(s) and a first set of parameter values for a parameterized automobile model. The generative design application then performs optimization operation(s) on the first set of parameter values based on the first set of metric values to generate a second set of parameter values. Subsequently, the generative design application generates at least one design based on the second set of parameter values that is more convergent with respect to at least one of the design goals than a previously generated design. Advantageously, less time and effort are required to generate and evaluate multiple designs and then optimize those designs relative to more manual prior art approaches.

公开(公告)号：US11568098B2

公开(公告)日：2023-01-31

申请号：US16414391

申请日：2019-05-16

Applicant: Autodesk, Inc.

Inventor： Zhihao Zuo ,  Shoudong Xu ,  Huagang Yu ,  Arpan Biswas ,  Nandakumar Santhanam

IPC: G06F30/17 ,  G06T17/10 ,  G06T19/20 ,  G06F30/23

Abstract: A method, apparatus, and system provide the ability to design a convective cooling channel in a computer. Input data is acquired and includes a geometry of an object to be cooled, a design objective, and boundary conditions. Channel designs corresponding to the input data are generated using an iterative topology optimization. One of the channel designs is selected and output.