Autodesk, Inc.

Damon Lau

David Benjamin

James Stoddart

Danil Nagy

Lorenzo Villaggi

Dale Zhao

Rui Wang

Lindsey Wikstrom

Daniel Mario Noviello

Martial Nabankele Somda

Bastian Schäfer

Techniques are disclosed for designing manufacturing facilities. A design application imports a computer-aided design (CAD) model and metadata associated with a manufacturing facility to generate a data set that specifies geometric and manufacturing constraints of the manufacturing facility. The design application performs optimization operations based on the data set to identify one or more high-performing designs that assign components to docks in the manufacturing facility. The optimization operations can include genetic optimization operations that generate multiple generations of designs, each of which is evaluated based on a travel distance, congestion, and number of turns associated with paths traversed by components for the design.

Genetic optimization techniques for designing manufacturing facilities

Techniques for accelerating the designing of manufacturing facilities

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.

Generative design techniques for automobile designs

A computer-implemented method for generating a building design for a building site having a complex shape comprises: generating a first group of processing areas from a plurality of processing areas associated with the building design and a second group of processing areas from the plurality of processing areas; generating a first building footprint leg that is sized to include the first group of processing areas and a second building footprint leg that is sized to include the second group of processing areas; at a pivot location, joining the first building footprint leg and the second building footprint leg at an aperture angle; performing a numerical simulation to position the first group of processing areas in the first building footprint leg and the second group of processing areas in the second building footprint leg to generate a candidate building design for the building site; and rendering the candidate building design.

Generating viable building designs for complex sites

A design analysis engine analyzes a CAD assembly to generate a topological model. The topological model is a graph of nodes coupled together via a set of edges. Each node represents a different CAD model in the CAD assembly and each edge represents a different connection between two such CAD models. The design analysis engine also analyzes the CAD assembly to generate a data model corresponding to the topological model. The data model is a structured dataset that includes component entries and connection entries. A component entry includes design data associated with a CAD model in the CAD assembly and a connection entry includes design data associated with a physical or logical connection between two or more such CAD models. A user interacts with the topological model to navigate the CAD assembly, obtain CAD model data, and initiate automatically-performed design tasks.

Techniques for generating graph-based representations of complex mechanical assemblies

Various embodiments set forth systems and techniques for assessing sustainability of a building design. The techniques include receiving a building design for a budding and sustainability certification data describing a plurality of sustainability certification systems; selecting one or more particular sustainability certification systems from the plurality of sustainability certification systems; computing a respective sustainability certification value for each sustainability certification system of the one or more particular sustainability certification systems; and performing one or more building design operations based on sustainability certification system values for the one or more particular sustainability certification systems.

Optimizing building design and architecture for sustainability certification

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.

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

Various embodiments set forth systems and techniques for determining expandability of a budding design. The techniques include receiving a building geometry indicating a size of a building and a shape of the building; receiving building placement data indicating a placement of the building within a site; identifying one or more remaining spaces within the site based at least on the size of the building, the shape of the building, and the placement of the building within the site; for each remaining space of the one or more remaining spaces: determining whether the remaining space is a usable space; based on the determining whether the remaining space is a usable space, generating a space expansion value corresponding to the remaining space; and computing, based on space expansion values corresponding to the one or more remaining spaces, one or more aggregate expansion values associated with the building geometry and the building placement.

Optimizing building design for future transformation and expansion

In various embodiments, an automobile modeling application generates automobile designs. In operation, the automobile modeling application determines a first parameter value associated with a first instance of a first parameterized automobile component. The automobile modeling application then computes a second parameter value associated with a second instance of a second parameterized automobile component based on a structural relationship between the first instance and the second instance. Subsequently, the automobile modeling application generates a computer-aided design (CAD) geometry model for an automobile based on the first parameter value, the second parameter value, and one or more functional relationships defined between two or more instances included in a set of parameterized automobile component instances. Advantageously, because the automobile modeling application automatically propagates changes throughout the automobile design, the amount of time required to make significant changes to the automobile design can be substantially reduced.

Techniques for generating comprehensive information models for automobile designs

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.

Rapid exploration of building design options for ventilation

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.

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New York, NY, United States of America

Damon Lau