Anomaly Detection in Curved Composites

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,999.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
N68335-10-C-0553
Agency Tracking Number:
N102-143-1129
Solicitation Year:
2010
Solicitation Topic Code:
N102-143
Solicitation Number:
2010.2
Small Business Information
Intelligent Optical Systems, Inc.
2520 W. 237th Street, Torrance, CA, 90505
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
033449757
Principal Investigator:
Indu Saxena
Senior Scientist
(424) 263-6346
sbirproposals@intopsys.com
Business Contact:
Reuben Sandler
CEO
(424) 263-6305
randdoffice@intopsys.com
Research Institution:
n/a
Abstract
The detection and characterization of strength-reducing features in composite structures such as rotor blades, notably in the H-53 Heavy Lift Helicopter program, is of critical importance to the Navy. Nondestructive inspection of bridging in tight bends of composites in thick sheets of laminates is one area where there is a lack of capability at the right cost. Intelligent Optical Systems (IOS) proposes Anomaly Detection in Curved Composites (ADeCC), based on detecting guided wave ultrasound for detecting and quantifying bridging over relatively large areas to significantly improve quality control of thick composite parts subject to such defects. An affordable, nondestructive tool is proposed using flexible optical fiber transducers for ultrasound collection that will reliably detect bridging in tight radii. The graphite/epoxy and fiberglass/epoxy laminates of geometries and up to 0.75 in. thick will be obtained from the National Composite Center in Kettering, OH for testing in Phase I. The goal is to detect bridging and other materials or obstructions between layers in samples of molded-in metal sections. After demonstration of feasibility, Phase II will focus on delivering a complete single-command operated hardware and software system that includes data analysis, with detection of bridging and a corresponding confidence level metric.

* information listed above is at the time of submission.

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