DESIGNING OBJECTS USING LATTICE STRUCTURE OPTIMIZATION

    公开(公告)号:US20200210629A1

    公开(公告)日:2020-07-02

    申请号:US16727862

    申请日:2019-12-26

    Applicant: AUTODESK, INC.

    Abstract: A design engine for designing an object using structural analysis. The design engine generates a lattice structure for the object comprising a plurality of nodes and a plurality of lines connecting the nodes. The lattice structure is optimized to remove one or more lines using structural analysis based on at least one load-related design requirement. Several design options are provided for generating and optimizing the lattice structure. The design engine then generates a 3D model of the object by thickening each line of the lattice structure into a pipe volume. The thickness of each pipe is determined using structural analysis based on the at least one load-related design requirement. The 3D model represents the volume of the object and is exportable to a fabrication device.

    Techniques for analyzing vehicle design deviations using deep learning with neural networks

    公开(公告)号:US11468292B2

    公开(公告)日:2022-10-11

    申请号:US16362555

    申请日:2019-03-22

    Applicant: AUTODESK, INC.

    Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

    Generative design for architecture

    公开(公告)号:US11409920B2

    公开(公告)日:2022-08-09

    申请号:US15812885

    申请日:2017-11-14

    Applicant: AUTODESK, INC.

    Abstract: A design engine includes a geometry module and a metric module that interoperate to generate optimal design options. The geometry module initially generates a spectrum of design options for a structure based on project constraints and design criteria set forth by potential occupants of the structure. The metric module then analyzes each design option and generates, for any given design option, a set of metrics that indicates how well the given design option meets the design criteria. The geometry module then generates additional design options in an evolutionary manner to improve the metrics generated for subsequent design options.

    Computer-implemented method for space frame design, space frame construction kit and space frame

    公开(公告)号:US11126760B2

    公开(公告)日:2021-09-21

    申请号:US16831671

    申请日:2020-03-26

    Applicant: AUTODESK, INC.

    Abstract: A computer-implemented method for space frame design involves constructing a load stress map in a geometrical boundary representation of a design space, defining attachment points and load application points in the design space, creating a starting network of interconnecting lines between each two of the attachment points and load application points in the design space, assigning load application factors to each line of the starting network of interconnecting lines based on values of the load stress map, generating potential space frame designs by culling different subsets of lines of the starting network of interconnecting lines for each potential space frame design according to variable culling parameters, evaluating the potential space frame designs with respect to optimization parameters, combining the culling parameters for the potential space frame designs the performance score of which is above a predefined performance threshold, and iterating the steps of generating potential space frame designs and evaluating the potential space frame designs on the basis of the combined culling parameters.

    COMPUTER-IMPLEMENTED METHOD FOR SPACE FRAME DESIGN, SPACE FRAME CONSTRUCTION KIT AND SPACE FRAME

    公开(公告)号:US20180011965A1

    公开(公告)日:2018-01-11

    申请号:US15713572

    申请日:2017-09-22

    Applicant: AUTODESK, INC.

    CPC classification number: G06F17/5086 B64C1/08 B64D11/0023 B64F5/00 Y02T50/46

    Abstract: A computer-implemented method for space frame design involves constructing a load stress map in a geometrical boundary representation of a design space, defining attachment points and load application points in the design space, creating a starting network of interconnecting lines between each two of the attachment points and load application points in the design space, assigning load application factors to each line of the starting network of interconnecting lines based on values of the load stress map, generating potential space frame designs by culling different subsets of lines of the starting network of interconnecting lines for each potential space frame design according to variable culling parameters, evaluating the potential space frame designs with respect to optimization parameters, combining the culling parameters for the potential space frame designs the performance score of which is above a predefined performance threshold, and iterating the steps of generating potential space frame designs and evaluating the potential space frame designs on the basis of the combined culling parameters.

    Generative design techniques for automobile designs

    公开(公告)号:US12190022B2

    公开(公告)日:2025-01-07

    申请号:US18047607

    申请日:2022-10-18

    Applicant: AUTODESK, INC.

    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.

    Techniques for analyzing vehicle design deviations using deep learning with neural networks

    公开(公告)号:US11842262B2

    公开(公告)日:2023-12-12

    申请号:US18045809

    申请日:2022-10-11

    Applicant: AUTODESK, INC.

    CPC classification number: G06N3/045 G06F17/15 G06F30/15 G06N3/08

    Abstract: A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.

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