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Composites Automation LLC SBIR Phase I Award, July 2019

A SBIR Phase I contract was awarded to Composites Automation LLC in July, 2019 for $149,976.0 USD from the U.S. Department of Defense and United States Air Force.

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Contents

sbir.gov/node/1856783
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
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Composites Automation LLC
0
Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
United States Air Force
United States Air Force
0
Award Type
SBIR0
Contract Number (US Government)
FA8650-19-P-51860
Award Phase
Phase I0
Award Amount (USD)
149,9760
Date Awarded
July 10, 2019
0
End Date
July 10, 2020
0
Abstract

Composites Automation LLC and partner University of Delaware Center for Composite Materials proposes to expand their previously developed software framework for void modeling in autoclave prepreg manufacturing to include process aids (vacuum bag, breathers, bleeders, release films) and their influence on the process, and develop methodologies to assess effects of material and technician variability on overall part quality. The modeling strategy combined ABAQUS pre-processing, thermal and diffusion solvers and post-processing capability with custom Usersubroutines that implemented models for void evolution and a custom USERMAT for prepreg material models. The proposed effort will identify key material property data for process aids (e.g. stretch and compaction under temperature and pressure, permeability etc.), document potential sources of human variability (e.g. placement accuracy of ply boundaries), and methods to characterize both properties and degree of variability. Experimental demonstrations will validate software predictions for selected composite part geometries. The ultimate goal is to create a modeling and simulation capability that can model thermoset prepreg processing across the composites industry, with a long-term focus on out-of-autoclave processes where the lack of positive pressure magnifies the importance of process-induced defect prediction capability.

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