The Boeing Company

Daniel S ReMine

Tyler Charles Staudinger

Huafeng Yu

Richard Joel Thompson

Sean Auffinger

Zachary D Jorgensen

Joshua David Kalin

Alan R Katz

Craig S Bosma

Oliver William Sykes

Kevin Vasko

A method and apparatus for performing automated supervision and inspection of an assembly process. The method is implemented using a computer system. Sensor data is generated at an assembly site using a sensor system positioned relative to the assembly site. A three-dimensional global map for the assembly site and an assembly being built at the assembly site is generated using the sensor data. A current stage of an assembly process for building an assembly at the assembly site is identified using the three-dimensional global map. A context for the current stage is identified. A quality report for the assembly is generated based on the three-dimensional global map and the context for the current stage.

Automated supervision and inspection of assembly process

A method and apparatus for manufacturing a part. The part is designed using a CAD system to generate a CAD part model of the part. Features of the part are identified from the CAD part model of the part. A parametric specification of the part is generated using the features of the part. The parametric specification of the part is saved as a parametric part model. The parametric part model is used to fabricate the part.

System and method of voxel based parametric specification for manufacturing a part

A method and apparatus for performing automated supervision and inspection of an assembly process. The method is implemented using a computer system. Sensor data is generated at an assembly site using a sensor system positioned relative to the assembly site. A current stage of an assembly process for building an assembly at the assembly site is identified using the sensor data. A context for the current stage is identified. A quality report for the assembly is generated based on the sensor data and the context for the current stage.

A method for certifying a design of a liquid container onboard a vehicle for manufacture of the liquid container is provided. The method includes generating a computer geometric model of the liquid container according to the design, and executable code to reproduce dynamics and operational parameters of the vehicle, with the dynamics of the vehicle including dynamics during a vehicle maneuver that is selectable from a database of vehicle dynamics for a plurality of vehicle maneuvers. The method includes executing a simulating application to perform a simulation of liquid sloshing in the liquid container onboard the vehicle is performed subject to the dynamics and operational parameters, and iterate the simulation to refine a prediction of loads and stresses on the liquid container from the simulation produced thereby. Locations within the liquid container more vulnerable to fatigue-risk are identified based on the prediction, and output for certification of the design.

Vehicular liquid container design and manufacture

Systems, methods, and apparatus for artificial intelligence-based manufacturing part design are disclosed. A system for designing a part comprises at least one processor configured: to encode the desired part design to generate an encoded desired part design; to identify a group of part designs within a space that is similar to the desired part design by comparing the encoded desired part design to encoded realized part designs, encoded imagined part designs, real metadata, and imagined metadata within the space; to generate an encoded optimal part design by analyzing the group of part designs according to objectives and weightings provided by a user; and to decode the encoded optimal part design to generate an optimal part design. Further, the system comprises a display configured to display, to the user, the optimal part design, which the user may use as a guide to modify the desired part design accordingly.

Artificial intelligence-based manufacturing part design

An apparatus for building an artificial-intelligence system is provided. The apparatus accesses images of a real-world scene and generates an image of a simulated object corresponding to a real-world object using a first generative adversarial network (GAN). The apparatus inserts the image of the simulated object into the images of the real-world scene to produce images of the real-world scene including the simulated object. The apparatus applies the images of the real-world scene including the simulated object to a second GAN to remove visual artifacts thereby producing a training set of images of the real-world scene including the simulated object. The apparatus trains an artificial-intelligence algorithm using the training set of images to build the artificial-intelligence system to detect the real-world object in further images of the real-world scene and outputs the artificial-intelligence system for deployment on an autonomous vehicle.

Growing community of inventors

Seattle, WA, United States of America

Daniel S ReMine