| Autor: |
Boccardi, Salvatore, Fierro, Gian Piero Malfense, Meo, Michele |
| Předmět: |
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| Zdroj: |
Structural Health Monitoring; Nov2021, Vol. 20 Issue 6, p2953-2962, 10p |
| Abstrakt: |
Structural health monitoring has become an important factor in the assessment of defects/damage in material components. Ultrasonic methods generally incorporate a sparse array of sensors/transducers as they provide a low number of piezoelectric sensors per area, thus providing savings with regard to system costs and weight. Many structural health monitoring techniques rely on linear ultrasonic effects such as reflections, amplitude changes, time of arrival and wave scattering effects which rely on precise baseline measurements. In this work, a nonlinear ultrasonic method based on a sparse array of surface-bonded ultrasonic transducers was used to evaluate the second harmonic and modulated elastic responses from a damaged medium. A complex composite stiffened panel with barely visible impact damage was evaluated. The points closest to damage are found on the paths between transmitter–receiver pairs through a reciprocal relationship of nonlinear elastic parameters and a statistical approach was used to select a cloud of points so that a two-dimensional image of the damaged region is created. Experimental results revealed that the second-order nonlinear parameter provided accurate damage localisation and imaging and the use of modulation bands further improved imaging accuracy and damage localisation. The maximum error between the calculated and the real damage area centres was only 1.3 mm. The proposed nonlinear elastic multi-path imaging technique based on higher harmonic generation and modulation coupled with a statistical approach provides damage detection without a priori knowledge of the material characteristics, which is in direct contrast to conventional linear ultrasonic methods which rely on precise measurement of elastic wave effects both before and after the initiation of damage. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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