Intelligent Damage Identification and Prognosis for Composite Structures

Award Information
Agency:
Department of Defense
Amount:
$99,664.00
Program:
SBIR
Contract:
FA8650-04-M-5014
Solitcitation Year:
2004
Solicitation Number:
2004.1
Branch:
Air Force
Award Year:
2004
Phase:
Phase I
Agency Tracking Number:
F041-141-1150
Solicitation Topic Code:
AF04-141
Small Business Information
IMPACT TECHNOLOGIES, LLC
125 Tech Park Drive, Rochester, NY, 14623
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
073955507
Principal Investigator
 Michael Roemer
 Director of Engineering
 (585) 424-1990
 mike.roemer@impact-tek.com
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Email: mark.redding@impact-tek.com
Research Institution
N/A
Abstract
Impact Technologies, in collaboration with the University of Dayton Research Institute (UDRI) and GE Aircraft Engines, propose to develop a practical yet innovative damage identification and prognosis system for composite structures that uses an optimized suite of reliable COTS sensors/actuators coupled with advanced damage detection and modeling algorithms. Robust and low-cost sensing systems such as piezoelectric devices, acoustic emission sensors, inherent resistivity measurements, and strain-based sensors will be assessed and down-selected to provide comprehensive insight into the structure's observed health state. Next, the measurements will be complemented with both micro- and macro-scale structural models to both provide virtual measurements for critical locations not directly measured and to represent the baseline health state of the structure to be used in the fault detection and isolation process. The proposed modeling methodology will also be augmented using adaptive model updating techniques that will use the available (or virtually sensed) measurements on the structure to continually assess the current damage state and predict future damage based on historical loading profiles. Specifically, model-based data fusion and an extended Kalman filter are proposed to continually update the FE and composite models so that the currently identified model parameters (potentially damaged) can be assessed against the healthy baseline model. Advanced statistical and neural network classifiers will be used to determine damage type, location and severity. A Phase I demonstration is planned that utilizes an integrated sensor suite and model-based structural health monitoring (SHM) process to detect, isolate and predict damage in a realistic carbon fiber reinforced polymer (CFRP) composite laminate.

* information listed above is at the time of submission.

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