The Boeing Company

Commonwealth Scientific and Industrial Research Organisation

Samuel J Tucker

Stephen Christensen

Jeffrey S Wiggins

Alexander Rubin

Wendy Wenjun Tian

Russell John Varley

Nguyen Buu Dao

Carla E Reynolds

Phillip John Crothers

Steven Michael Shewchuk

Jack J Esposito

Gregg Robert Bogucki

Troy Winfree

Andrew Lawrence Bauer

Joshua David Kalin

Dustin D Fast

Carla Elizabeth Reynolds

A method of manufacturing a composite structure includes accessing design data for the composite structure that is manufactured according to a process including forming a layup of plies of fibers using a machine tool. The method includes applying the design data to an ANN classifier to classify a localized inconsistency of a type of inconsistency on the composite structure, the localized inconsistency spatially referenced to a location on the composite structure. The method includes performing a root cause analysis to identify one or more of process parameters as a potential cause of the type of inconsistency, and modifying one or more of the geometric model, the layup design, or values of the one or more of the process parameters to address the potential cause.

Composite manufacturing using data analytics

The present disclosure relates to epoxide containing compounds comprising three benzene units linked by bridging groups. The disclosure also relates to the production of curable epoxy resin formulations comprising said epoxide containing compounds, and their possible incorporation into composite materials such as fibre reinforced composites. Possible methods for formulating the compounds epoxide containing compounds, as described herein, are also disclosed.

Epoxy resin

A system to aid in design for manufacturing an object includes a processor and a memory configured to store instructions. The processor is configured to receive first data representing a design of the object to be manufactured and second data representing a machine-learning model. The processor is configured to execute the instructions to generate third data using the first data and the second data. The third data indicates at least one of a modification to the design of the object or process conditions for production of the object. The processor is configured to send the design of the object, the process conditions, or both, to a manufacturing tool to enable production of the object. The machine-learning model is representative of production data and based at least partially on one or more of: object features, process parameters, environmental factors, and quality data.

Object design using machine-learning model

Methods and formulations for distortional thermosets are disclosed that display enhanced composite mechanical performances and robust sorption resistance. The composition includes an epoxy resin of formula (I): and a diamine curing agent. The resultant distortional thermoset compositions possess superior out-life requirements and advantageous reaction kinetics for preparing prepreg compositions and materials.

Formulation methodology for distortional thermosets

A composite test coupon includes a plurality of plies. The plurality of plies include first ply layers and second ply layers. The first ply layers have first fibers and a substantially uniform matrix material associated with the first fibers. The second ply layers have second fibers and a pre-stressed matrix material associated with the second fibers. The first fibers are oriented in a first direction, and the second fibers are oriented in a second direction that is different from the first direction. The pre-stressed matrix material includes stress induced cracks between the second fibers of each of the second ply layers.

Pre-strained composite test coupons

Growing community of inventors

St. Louis, MO, United States of America

Samuel J Tucker