Mr. David Kukhalashvili Profile

David Kukhalashvili

Research Assistant at New Mexico Institute of Mining and Technology

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Proceedings Article | 9 April 2010 Paper

Magneto elastic active sensors for structural health monitoring using magneto-mechanical impedance and elastic wave propagation

Timothy Barnes, Davit Kukhalashvili, Andrei Zagrai

Proceedings Volume 7650, 76501L (2010) https://doi.org/10.1117/12.847806

KEYWORDS: Brain-machine interfaces, Sensors, Transducers, Structural health monitoring, Active sensors, Aluminum, Wave propagation, Structural dynamics, Near field, Ultrasonics

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Magneto-elastic active sensors (MEAS) offer an alternative to piezoelectric wafer active sensors (PWAS) for structural health monitoring (SHM) applications. In essence, a MEAS consists of a coil of wire carrying a timevarying electrical current in the presence of a static magnetic field. The Lorentz-force mechanism facilitates transduction without a mechanical bond between the sensor and the host structure, thereby circumventing some of the shortcomings of PWAS. In this paper, the development of MEAS is briefly recounted and applications of MEAS to SHM are presented. The miniaturization of MEAS for improved embeddability is also discussed. The ability of MEAS to detect loose bolts by the pitch-catch method is presented. MEAS application for near-field and far-field crack detection is also explored. Finally, the utilization of MEAS in Magneto-Mechanical Impedance (MMI) method is discussed. The MMI technique provides a means of assessing the integrity of metallic structures through measurement of structural dynamic response. Since structural damage affects mechanical properties, it modifies structural dynamic characteristics reflected in MMI signature. The use of MMI to monitor fatigue damage in aluminum alloys is presented. Aluminum samples were subjected to cyclic loading in increments of 10,000 cycles until cracks appeared. The MMI responses show downward frequency shift of impedance peaks as samples deteriorate under fatigue loading, confirming the capability of MMI techniques to detect incipient fatigue damage. Thus, the applicability of MEAS to various SHM techniques is demonstrated, and the advantages and disadvantages of MEAS are explored.

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