The Boeing Company

Progress Software Corporation

Ashutosh Mani

Shyamantak Gautam

Santosh Kumar

Matthew S Stauffer

Amir Afrasiabi

Ruban Phukan

Hieu Tat Nguyen

Shabnam Zangeneh-Khamooshi

Russell A Strope

Michael S Lindgren

Kayley E Seawright

Daniel E Gonsor

A method for computer analysis of a quality of an as-programmed surface of a composite laminate. A first data set representing an as-programmed top surface is generated based on as-programmed ply definitions and a tool surface definition. Thereafter, a second data set representing coordinates of points of a first mesh on the as-programmed top surface is generated, which points form a first mesh. Then a third data set representing coordinates of points of a second mesh on a defined tool surface is generated. A respective angle of each mesh element of the first mesh relative to a corresponding mesh element of the second mesh is then calculated. Each angle is compared to a threshold of acceptable angle. In response to an acceptable number of angles exceeding a threshold of acceptable angle, a tow placement machine may be programmed to fabricate a composite structure using the as-programmed ply definitions.

Method for computer analysis of as-programmed surface quality of composite structure

A system and method are disclosed for bottom-up modeling and prediction of asset anomalies. In one embodiment, the system receives first sensor data from a first sensor, the first sensor associated with a first asset; smooths the first sensor data; determines a first stage within sensor data, the sensor data including the first sensor data; determines a first set of anomalies within the first stage; generates a first asset state space associated with the first asset; trains a base model describing a first group of one or more assets, the first group of one or more assets including the first asset; and trains, using the base model, a final model particular to the first asset.

Scalable and bottom-up approach to automated anomaly detection

According to various embodiments, a technique for automated aircraft paint application quality inspection is presented. The technique includes retrieving from electronic persistent storage a control image depicting at least a portion of aircraft paint application for the aircraft under quality inspection; capturing a process image depicting at least a portion of aircraft paint application on the aircraft under quality inspection; detecting a plurality of features in the control image and the process image; registering the control image with the process image; detecting at least one difference between the control image and the process image; generating an output image, where the output image includes a depiction of the aircraft under quality inspection with the at least one difference annotated; and causing the output image to be displayed.

Automated paint quality control for aircraft

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Summerville, SC, United States of America

Ashutosh Mani