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1.发明申请公开(公告)号：US20190197198A1
公开(公告)日：2019-06-27
申请号：US15854234
申请日：2017-12-26
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Francesco IORIO , Adrian BUTSCHER
IPC: G06F17/50
CPC classification number: G06F17/504 , G06F17/5086 , G06F17/5095 , G06F2217/02 , G06F2217/04 , G06F2217/06 , G06F2217/12
Abstract: A design engine automates portions of a mechanical assembly design process. The design engine generates a user interface that exposes tools for capturing input data related to the design problem. Based on the input data, the design engine performs various operations to generate a formalized problem definition that can be processed by a goal-driven optimization algorithm. The goal-driven optimization algorithm generates a spectrum of potential design options. Each design option describes a mechanical assembly representing a potential solution to the design problem.
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2.发明申请公开(公告)号：US20210342046A1
公开(公告)日：2021-11-04
申请号：US17377036
申请日：2021-07-15
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER
IPC: G06F3/0484 , G06F30/00
Abstract: A design engine implements a probabilistic approach to generating designs that exposes automatically-generated design knowledge to the user during operation. The design engine interactively generates successive populations of designs based on a problem definition associated with a design problem and/or a previously-generated population of designs. During the above design process, the design engine generates a design knowledge graphical user interface (GUI) that graphically exposes various types of design knowledge to the user. In particular, the design engine generates a design variable dependency GUI that visualizes various dependencies between designs variables. The design engine also generates a design evolution GUI that animates the evolution of designs across the successive design populations. Additionally, the design engine generates a design exploration GUI that facilitates the user exploring various statistical properties of automatically-generated designs.
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公开(公告)号：US20210124852A1
公开(公告)日：2021-04-29
申请号：US17141060
申请日：2021-01-04
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Francesco IORIO , Adrian BUTSCHER
IPC: G06F30/17
Abstract: A design engine systematically explores a design space associated with a design problem related to mechanical assemblies. The design engine implements a constraint programming approach to produce mechanical assembly configurations that adhere to a set of design constraints. For each feasible configuration, the design engine then optimizes various parameters to generate design options that meet a set of design objectives. With these techniques, the design space can be explored very quickly to generate significantly more feasible design options for the mechanical assembly than possible with conventional manual approaches. Accordingly, numerous design options can be generated that may otherwise never be produced using those conventional approaches.
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公开(公告)号：US20250148171A1
公开(公告)日：2025-05-08
申请号：US18821966
申请日：2024-08-30
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER , Hesam SALEHIPOUR
Abstract: In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
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5.发明申请公开(公告)号：US20230082505A1
公开(公告)日：2023-03-16
申请号：US18058210
申请日：2022-11-22
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER
IPC: G06F3/0484 , G06F30/00
Abstract: A design engine implements a probabilistic approach to generating designs that exposes automatically-generated design knowledge to the user during operation. The design engine interactively generates successive populations of designs based on a problem definition associated with a design problem and/or a previously-generated population of designs. During the above design process, the design engine generates a design knowledge graphical user interface (GUI) that graphically exposes various types of design knowledge to the user. In particular, the design engine generates a design variable dependency GUI that visualizes various dependencies between designs variables. The design engine also generates a design evolution GUI that animates the evolution of designs across the successive design populations. Additionally, the design engine generates a design exploration GUI that facilitates the user exploring various statistical properties of automatically-generated designs.
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Patent Agency Ranking
公开(公告)号：US20200334337A1
公开(公告)日：2020-10-22
申请号：US16434085
申请日：2019-06-06
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER
Abstract: A design engine implements a probabilistic approach to generating designs for computer-aided design (CAD) assemblies. The design engine initially generates a population of designs based on a problem definition associated with a design problem. Each design includes a randomly-generated set of design values assigned to various design variables. The design engine repairs any infeasible designs in the population of designs and then executes a dynamic simulation with the population of designs. The design engine selects the most performant designs and identifies, based on those performant designs, design variables that are dependent on one another. The design engine generates a probability model indicating conditional probabilities between design values associated with dependent design variables. The design engine then iteratively samples the probability model to generate a subsequent population of designs. In this manner, the design engine can automatically generate designs for mechanical assemblies significantly faster than possible with conventional algorithmic design techniques.
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公开(公告)号：US20190213300A1
公开(公告)日：2019-07-11
申请号：US15866398
申请日：2018-01-09
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Francesco IORIO , Adrian BUTSCHER
IPC: G06F17/50
CPC classification number: G06F17/5086 , G06F17/5095 , G06F2217/06
Abstract: A design engine systematically explores a design space associated with a design problem related to mechanical assemblies. The design engine implements a constraint programming approach to produce mechanical assembly configurations that adhere to a set of design constraints. For each feasible configuration, the design engine then optimizes various parameters to generate design options that meet a set of design objectives. With these techniques, the design space can be explored very quickly to generate significantly more feasible design options for the mechanical assembly than possible with conventional manual approaches. Accordingly, numerous design options can be generated that may otherwise never be produced using those conventional approaches.
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公开(公告)号：US20180345647A1
公开(公告)日：2018-12-06
申请号：US15996407
申请日：2018-06-01
Applicant: AUTODESK, INC.
Inventor： Nigel MORRIS , Adrian BUTSCHER , Francesco IORIO
Abstract: Design process that performs geometry synthesis on a 3D model of a product based on a design problem statement and manufacturing constraints associated with a manufacturing machine intended to manufacture the product. The manufacturing constraints may include dimensions for a tool bit, dimensions for a tool head, a set of machining directions of the manufacturing machine, or any combination thereof. For a 5-axis manufacturing machine, the set of machining directions may be determined by a “NormalSearch” algorithm and/or a “HeatSearch” algorithm. The geometry synthesis produces a design solution comprising a final 3D model of the product, whereby each point on the boundary of the final 3D model is determined to be accessible by a tool bit and/or tool head in at least one machining direction of the manufacturing machine. Thus, the design solution for the product is more easily and directly manufacturable by the manufacturing machine.
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公开(公告)号：US20250148162A1
公开(公告)日：2025-05-08
申请号：US18821981
申请日：2024-08-30
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER , Hesam SALEHIPOUR
IPC: G06F30/20
Abstract: In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
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公开(公告)号：US20250148161A1
公开(公告)日：2025-05-08
申请号：US18821940
申请日：2024-08-30
Applicant: AUTODESK, INC.
Inventor： Hyunmin CHEONG , Mehran EBRAHIMI , Adrian BUTSCHER , Hesam SALEHIPOUR
IPC: G06F30/20
Abstract: In various embodiments, a generative design application can leverage multi-disciplinary optimization to solve a design problem associated with a 3D model and interdependent design variables. The generative design application includes an optimization engine that can solve the design problem, or portions thereof, by iteratively performing optimization techniques on the interdependent design variables to generate an 3D model that maximizes or minimizes one or more objectives and meets one or more constraints, while simultaneously considering the effect of each interdependency of the design variables. Furthermore, the generative design application can leverage a natural language interface to augment the creation of the design problem for optimization. The generative design application can also leverage free-form deformation during generative design to parameterize a portion of the 3D model as part of the design problem.
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