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Application of artificial neural networks to composite ply micromechanicsArtificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.
Document ID
19910015032
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Brown, D. A.
(Wooster Coll. OH., United States)
Murthy, P. L. N.
(NASA Lewis Research Center Cleveland, OH, United States)
Berke, L.
(NASA Lewis Research Center Cleveland, OH, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1991
Subject Category
Composite Materials
Report/Patent Number
NASA-TM-104365
E-6162
NAS 1.15:104365
Meeting Information
Meeting: Engineering Mechanics Conference
Location: Columbus, OH
Country: United States
Start Date: May 19, 1991
End Date: May 22, 1991
Sponsors: American Society of Civil Engineers
Accession Number
91N24345
Funding Number(s)
PROJECT: RTOP 307-50-00
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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