Abstract:
A design application generates feasible engineering designs that satisfy criteria associated with a particular engineering problem. The design application receives input that outlines a specific engineering problem to be solved, and then synthesizes a problem specification based on this input. The design application then searches a database to identify different classes of approaches to solving the design problem set forth in the problem specification. The design application then selects one or more such classes of approaches, and generates a spectrum of potential design solutions for each such approach. The generated solutions may then be evaluated to determine the degree to which the problems specification has been met.
Abstract:
A design application allows an end-user to define an engineering problem, and then synthesizes a spectrum of design options that solve the engineering problem. The design application then generates various tools to allow the end-user to explore that spectrum of design options. The design application allows the end-user to compare various attributes of each design option, and to filter the spectrum of design options based on those attributes. In response to end-user selections of certain design options, the design application identifies other similar design options, and then displays these design options to the end-user.
Abstract:
A design application interacts with an end-user to generate design problem geometry that reflects a design problem to be solved. Various design objectives, design constraints, boundary conditions, and other design criteria may be associated with the design problem geometry via the design application. When the design problem is sufficiently well defined, a client-side solver generates a solution approximation using a coarse multi-objective solver. The client-side solver favors speed over accuracy, and so the solution approximation provides only a rough representation of various attributes of potentially feasible design solutions. Based on the solution approximation, the end-user may correct any omissions, mistakes, and so forth, before executing pay-per-service cloud-based parallel solver.
Abstract:
A design application includes a design engine and a tracking engine. The design engine allows end-users to create and modify a design space. The design space includes a spectrum of possible design options, as well as other information related to the process of creating designs. When changes are applied to the design space, the design engine transmits event data to the tracking engine that reflects those changes. The tracking engine, based on the event data, updates a design space timeline. The design space timeline illustrates the evolution of the design space over time.
Abstract:
A design application generates feasible engineering designs that satisfy criteria associated with a particular engineering problem. The design application receives input that outlines a specific engineering problem to be solved, and then synthesizes a problem specification based on this input. The design application then searches a database to identify different classes of approaches to solving the design problem set forth in the problem specification. The design application then selects one or more such classes of approaches, and generates a spectrum of potential design solutions for each such approach. The generated solutions may then be evaluated to determine the degree to which the problems specification has been met.
Abstract:
A design application is configured to determine design problem geometry and design criteria associated with a design problem to be solved. Based on this information, the design application identifies one or more design approaches to creating a custom material having specific material attributes needed to solve the design problem. The design application then executes the design approaches to create material designs that reflect one or more custom materials. With these designs as input, a manufacturing machine may then construct physical instances of those custom materials. A given custom material may have a unique combination of material attributes potentially not found among existing materials. Additionally, a design fabricated from a custom material may better satisfy the design criteria than a design fabricated from a known material.
Abstract:
A design application generates a spectrum of design options that meet certain design criteria. Each design option may potentially be composed of a different type of material. The design application filters the spectrum of design options for presentation in a graphical user interface (GUI). The GUI illustrates different design options based on material of composition within a parallel axis plot that includes separate axes for different material attributes. The GUI also displays envelopes of design options for each different material or material type, where each envelope has a different color, pattern, opacity, or other visual attribute. A GUI engine dynamically updates the GUI to reflect constraints and other design criteria applied to the spectrum of design options.
Abstract:
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.
Abstract:
An iterative design environment performs an iterative design process of a product by implementing usage feedback of the product when utilized under real-world conditions. Sensors are installed on the physical product and collect data about the behavior of the product under real-world conditions. The sensor data comprise usage feedback implemented to inform and produce a design problem statement and one or more design solutions. The sensor data is received by a problem statement engine to produce a problem statement based, at least in part, on the sensor data. A design engine then produces one or more design solutions for the problem statement and one of the design solutions is fabricated to produce a new physical product. Sensors are then installed onto the new physical product and the iterative design process may be performed again. The iterative design process may be performed multiple times until a satisfactory physical product is achieved.
Abstract:
A design application includes a design engine and a tracking engine. The design engine allows end-users to create and modify a design space. The design space includes a spectrum of possible design options, as well as other information related to the process of creating designs. When changes are applied to the design space, the design engine transmits event data to the tracking engine that reflects those changes. The tracking engine, based on the event data, updates a design space timeline. The design space timeline illustrates the evolution of the design space over time.