Autodesk, Inc.

Francesco Iorio

Hyunmin Cheong

Wei Li

Adrian Butscher

Mehran Ebrahimi

Nigel Morris

Massimiliano Moruzzi

Adrian Adam Thomas Butscher

Nigel Jed Wesley Morris

Mark Thomas Davis

Michael Bergin

Mehdi Nourbakhsh

Karl Willis

Erin Marie Bradner

Alexander Tessier

Benjamin David Cochran

Ali Baradaran Hashemi

Abhishek Trivedi

Claude-Guy Quimper

Niels Grafen

Michael Tao

John Yee

Pavel Babikov

David Thornton

Vincent Goulet

A centralized design engine receives a problem specification from an end-user and classifies that problem specification in a large database of previously received problem specifications. Upon identifying similar problem specifications in the large database, the design engine selects design strategies associated with those similar problem specifications. A given design strategy includes one or more optimization algorithms, one or more geometry kernels, and one or more analysis tools. The design engine executes an optimization algorithm to generate a set of parameters that reflect geometry. The design engine then executes a geometry kernel to generate geometry that reflects those parameters, and generates analysis results for each geometry. The optimization algorithms may then improve the generated geometries based on the analysis results in an iterative fashion. When suitable geometries are discovered, the design engine displays the geometries to the end-user, along with the analysis results.

Goal-driven computer aided design workflow

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.

Topology optimization techniques for three-dimensional models

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.

Techniques for applying generative design to the configuration of mechanical assemblies

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.

Constraint-oriented programming approach to mechanical assembly design

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.

Topology optimization for subtractive manufacturing techniques

A design engine generates a configuration option that includes a specific arrangement of interconnected mechanical elements adhering to one or more design constraints. Each element within a given configuration option is defined by a set of design variables. The design engine implements a parametric optimizer to optimize the set of design variables associated with each configuration option. For a given configuration option, the parametric optimizer discretizes continuous equations governing the physical dynamics of the configuration. The parametric optimizer then determines the gradient of an objective function based on the discretized equations the gradient of objective and constraint functions based on discrete direct differentiation method or discrete adjoint variable method derived directly from the discretized motion equations. Then, the parametric optimizer traverses a design space where the configuration option resides to reduce improve the objective function, thereby optimizing the design variables.

Efficient sensitivity analysis for generative parametric design of dynamic mechanical assemblies

A design application receives an exemplary design from an end-user having one or more functional attributes relevant to solving a design problem. The design application then generates a set of labels that describes the functional attributes of the exemplary design. Based on the set of labels, the design application explores a functional space to retrieve one or more system classes having functionally descriptive labels that are similar to the set of labels generated for the exemplary design. The one or more system classes include different approaches to solving the design problem, and represent systems having at least some functional attributes in common with the exemplary design.

Deep-learning based functional correlation of volumetric designs

A design application is configured to perform a system-level optimization of a collection of system components. The design application iteratively executes a multi-objective solver to optimize structural and functional relationships between the system components in order to meet global design criteria and generate a system design. The design application initializes the design process by extracting from a knowledge base system templates having taxonomic, structural, or functional attributes relevant to the system design. The design application generates the knowledge base by mining taxonomic, structural, and functional relationships from a corpus of engineering texts.

System-level approach to goal-driven design

In one embodiment, a model generator generates a new model for a behavior of a system based on an existing, authoritative model. First, a mapping generator generates a mapping model that maps authoritative values obtained via the authoritative model to measured values that represent the behavior of the system. Subsequently, the model generator creates the new model based on the authoritative model and the mapping model. In this fashion, the mapping model indirectly transforms the authoritative model to the new model based on the measured values. Advantageously, the authoritative model enables the model generator to increase a rate of accuracy improvement experienced while developing the new model compared to a rate of accuracy improvement that would be experienced were the new model to be generated based on conventional modeling techniques. In particular, for a given sampling budget, the model generator improves the accuracy of the new model.

Techniques for modeling behaviors of systems via transformations of authoritative models

Embodiments of the invention disclosed herein provide techniques for generating an alternative design recommendation. The techniques include determining, via a processor, a first function associated with one or more first components included in a first design. The techniques further include analyzing, via the processor, a plurality of components included in a database to select one or more second components associated with a second function that corresponds to the first function. The techniques further include causing an alternative design recommendation that includes the one or more second components to be output for display.

Knowledge-based alternative design recommendations

A design engine consolidates portions of a mechanical assembly design to reduce the number of components included in the design. The design engine analyzes the design to determine various criteria associated with the assembly. Then, the design engine identifies a group of components within the design to be consolidated. The design engine determines a volumetric region where the group of components resides and then subdivides the volumetric region. The design engine then initiates a generative design process based on the determined criteria to create geometry within each subdivision of the volumetric region. The newly generated geometry includes fewer components than the initial group of components. The design engine then replaces the group of components with the newly generated geometry, thereby consolidating the group and reducing the total number of components included in the design.

Simplifying designs of mechanical assemblies via generative component consolidation

Embodiments of the invention disclosed herein provide techniques for generating a design autocomplete recommendation. The techniques include determining one or more first components included in a first design. The techniques further include analyzing, via the processor, a plurality of designs included in a database to select a second design that includes one or more second components that correspond to the one or more first components. The techniques further include selecting, via a processor, one or more third components included in the second design that are not included in the first design. The techniques further include causing an autocomplete recommendation that includes the one or more third components to be output.

Knowledge-based design autocomplete recommendations

A computing device for receiving a design problem statement describing a design problem in a controlled natural language (CNL) that defines permitted lexicons and syntax structures. The design problem statement is processed using the CNL lexicons and syntax structures to produce a job description executable by a design application for generating a design solution for the design problem statement. An improved CNL user interface that assists users to produce valid design problem statements that are CNL-compliant. The CNL user interface receives user-selectable terms that are compliant with the CNL lexicons and generates candidate problem statements that are compliant with CNL syntax structures and receives a selection of a candidate problem statement that is added to the design problem statement. A graphical user interface may display a graphical representation of a design problem statement that can be directly modified. A dialogue-based design process to explore possible design intentions and design solutions.

Techniques for using controlled natural language to capture design intent for computer-aided design

A design engine analyzes a complex polygonal mesh to identify regions of that mesh that can be simplified. The design engine then replaces those identified regions with simplified geometry that is more easily fabricated using traditional techniques. The remaining complex regions of the mesh are fabricated using additive fabrication techniques. The design engine interacts with both a traditional fabrication device and an additive fabrication device to fabricate the simplified and complex regions of the mesh, respectively. In this manner, a hybrid 3D structure is generated that includes both simplified geometry and complex geometry.

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Toronto, Canada

Francesco Iorio