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Proceedings Paper

Early detection of local buckling in structural members
Author(s): Bashir Ali; Mannur J. Sundaresan; Mark J. Schulz; Derke Hughes
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Paper Abstract

Most structural health monitoring analyses to date have focused on the determination of damage in the form of crack growth in metallic materials or delamination or other types of damage growth in composite materials. However, in many applications, local instability in the form of buckling can be the precursor to more extensive damage and unstable failure of the structure. If buckling could be detected in the very early stages, there is a possibility of taking preventive measures to stabilize and save the structure. Relatively few investigations have addressed this type of damage initiation in structures. Recently, during the structural health monitoring of a wind turbine blade, local buckling was identified as the cause of premature failure. A stress wave propagation technique was used in this test to detect the precursor to the buckling failure in the form of early changes in the local curvature of the blade. These conditions have also been replicated in the laboratory and results are reported in this paper. A composite column was subjected to axial compression to induce various levels of buckling deformation. Two different techniques were used to detect the precursors to buckling in this column. The first identifier is the change in the vibration shapes and natural frequencies of the column. The second is the change in the characteristics of diagnostic Lamb waves during the buckling deformation. Experiments indicate that very small changes in curvature during the initial stages of buckling are detectable using the structural health monitoring techniques. The experimental vibration characteristics of the column with slight initial curvatures compared qualitatively with finite element results. The finite element analysis is used to identify the frequencies that are most sensitive to buckling deformation, and to select suitable locations for the placement of sensors that can detect even small changes in the local curvature.

Paper Details

Date Published: 17 May 2005
PDF: 7 pages
Proc. SPIE 5764, Smart Structures and Materials 2005: Smart Structures and Integrated Systems, (17 May 2005); doi: 10.1117/12.600152
Show Author Affiliations
Bashir Ali, North Carolina A&T State Univ. (United States)
Mannur J. Sundaresan, North Carolina A&T State Univ. (United States)
Mark J. Schulz, Univ. of Cincinnati (United States)
Derke Hughes, Naval Undersea Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 5764:
Smart Structures and Materials 2005: Smart Structures and Integrated Systems
Alison B. Flatau, Editor(s)

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