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TECHNICAL PAPERS

A Potential Link from Damage Diagnostics to Health Prognostics of Composites through Built-in Sensors

[+] Author and Article Information
Fu-Kuo Chang

Department of Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Durand Building, Stanford, CA 94305fkchang@stanford.edu

Johannes F. Markmiller, Jeong-Beom Ihn, Kok Yen Cheng

Department of Aeronautics and Astronautics, Stanford University, 496 Lomita Mall, Durand Building, Stanford, CA 94305

J. Vib. Acoust 129(6), 718-729 (Sep 29, 2006) (12 pages) doi:10.1115/1.2730530 History: Received February 19, 2006; Revised September 29, 2006

This paper explores the potential of integration of damage diagnostics based on built-in sensors with a progressive failure modeling for monitoring and prediction of composite structures, from damage initiation to final failure while they are in service. A piezobased structural health monitoring system was utilized to monitor the damage initiation based on acoustic signals and to detect its growth based on ultrasonic waves generated by the piezoelectric sensors. Utilizing a damage index and an imaging algorithm, damage initiation and damage extent were estimated, respectively. A finite element code (ABAQUS ) based on a progressive failure analysis was adopted to simulate damage initiation and propagation in composites under a given loading condition. The numerical data and the diagnostic images were compared to x-ray pictures of a test coupon to verify the results. The results of the study strongly indicate that damage diagnostics and health prognostics could potentially be integrated to produce a powerful tool for managing the operation of composite structures in service.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Proposed conceptual study

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Geometry of the coupon

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Typical measured AE signal

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Paths used for active imaging

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Predicted load displacement curve

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Critical damage events

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Comparisons: Imaging, x-ray, and simulation, 76% failure load

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Figure 8

Comparisons: Imaging, x-ray, and simulation, 78% failure load

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Figure 9

Comparisons: Imaging, x-ray, and simulation, 81% failure load

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Figure 10

Comparisons: Imaging, x-ray, and simulation, 84% failure load

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Figure 11

Comparisons: Imaging, x-ray, and simulation, 87% failure load

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Figure 12

Comparisons: Imaging, x-ray, and simulation, 96% failure load

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Figure 13

Comparisons: Imaging, x-ray, and simulation, 100% failure load

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