Abstract: A system for autonomous generative design in a system having a digital twin graph a requirements distillation tool for receiving requirements documents of a system in human-readable format and importing useful information contained in the requirements documents into the digital twin graph, and a synthesis and analysis tool in communication with the digital twin graph, wherein the synthesis and analysis tool generates a set of design alternatives based on the captured interactions of the user with the design tool and the imported useful information from the requirements documents. The system may include includes a design tool with an observer for capturing interactions of a user with the design tool, In addition to the observer, an insighter in communication with the design tool and with the digital twin graph receives design alternatives from the digital twin graph and present the receive design alternatives to a user via design tool.

Abstract: A method includes receiving, via a first component in a production environment, a sensor measurement corresponding to a second component in the production environment. A first digital twin corresponding to the first component is identified, and a perception algorithm is applied to identify a component type associated with the second component. A second digital twin is selected based on the component type, and a third digital twin is selected that models interactions between the first digital twin and the second digital twin. The third digital twin is used to generate instructions for the first component that allow the first component to interact with the second component. The instructions may then be delivered to the first component.

Abstract: Machine-learned networks provide generative design. Rather than emulate the typical human design process, an inverse model is machine trained to generate a design from requirements. A simulation model is machine trained to recover performance relative to the requirements for generated designs. These two machine-trained models are used in an optimization that creates further designs from the inverse model output design and tests those designs with the simulation model. The use of machine-trained models in this loop for exploring many different designs decreases the time to explore, so may result in a more optimal design or better starting designs for the design engineer.

Abstract: A method for designing a product comprising physical hardware and a controller for controlling the physical hardware includes generating a detailed hardware model describing the physical hardware and a controller model corresponding to the controller based on a set of user requirements. An optimal design of the physical hardware and the controller is determined by solving a multi-objective optimization problem comprising a first objective function defining hardware design objectives, and a second objective function defining objectives for the controller model for the physical hardware.

Abstract: Systems (500) and methods (400) for an interactive system for automatic generation, analysis and exploration of composable system of systems based on knowledge graphs. A method (400) includes receiving (405) a scenario (110) and a domain ontology (111); determining (410) structures (132), attributes (133), and capabilities (131) from the domain ontology; generating (415) design alternatives (146) based on the scenario using the structures, attributes, and capabilities; performing (430) an evaluation (159) of the design alternatives based on the scenario; generating (445) an SoS design (300) based on the evaluation; and displaying the SoS design to a user.

Abstract: A method for designing a product comprising physical hardware and a controller for controlling the physical hardware includes generating a detailed hardware model describing the physical hardware and a controller model corresponding to the controller based on a set of user requirements. An optimal design of the physical hardware and the controller is determined by solving a multi-objective optimization problem comprising a first objective function defining hardware design objectives, and a second objective function defining objectives for the controller model for the physical hardware.

Abstract: Computer assisted design data is rendered in the cloud. A client-server relationship is provided for 3D rendering. To reduce the burden on the server, the 3D rendering adapts based on the client capabilities. Where possible, some of the 3D rendering is performed by the server and some by the client machine. The 3D rendering by the client machine may be limited to avoid transfer of geometrical data of the CAD data. Different textures or shaders are used for rendering images associated with motion. Dictionary information is accumulated by the client machine to reduce the number of coefficients later transferred to the client machine for 3D rendering. The model represented by the CAD data is used to predict rendered images so that video compression may be performed. The server sparsely renders an image and compressive sensing is used by the client machine to generate the complete image.

Abstract: Methods for product data management and corresponding systems and computer-readable mediums. A method includes receiving a solid model. The method includes analyzing the solid model to determine a suggested orientation that minimizes a build height or minimizes a support volume. The method includes displaying and saving the suggested orientation.

Abstract: Systems and methods for support structures for additive manufacturing of solid models. A method includes receiving a solid model, for a physical object to be manufactured, that includes a plurality of boundary representation surfaces. The method includes analyzing the b-rep surfaces to generate point samples for potential support locations. The method includes clustering points on the solid model, corresponding to at least some of the point samples, to create support locations. The method includes generating column supports in the solid model that connect to the original solid model at the support locations. The method includes storing the solid model, including the column supports.

Abstract: Computer assisted design data is rendered in the cloud. A client-server relationship is provided for 3D rendering. To reduce the burden on the server, the 3D rendering adapts based on the client capabilities. Where possible, some of the 3D rendering is performed by the server and some by the client machine. The 3D rendering by the client machine may be limited to avoid transfer of geometrical data of the CAD data. Different textures or shaders are used for rendering images associated with motion. Dictionary information is accumulated by the client machine to reduce the number of coefficients later transferred to the client machine for 3D rendering. The model represented by the CAD data is used to predict rendered images so that video compression may be performed. The server sparsely renders an image and compressive sensing is used by the client machine to generate the complete image.