Damage detection by using features of nonlinear ultrasonic modulation in vibrating structures

Sunetchiieva, Sevilia; KU Leuven; Belgium

Sunetchiieva, S.; University of Leuven; Belgium
Pfeiffer, H.; University of Leuven; Belgium
Creten, S.; University of Leuven; Belgium
Wevers, M.; University of Leuven; Belgium
Glorieux, C.; University of Leuven; Belgium

ID: ECNDT-0595-2018
Download: PDF
Session: Nonlinear Ultrasonics 1
Room: G2
Date: 2018-06-12
Time: 11:30 - 11:50

Due to the high need for structural health monitoring in aerospace applications, numerous, quite mature linear ultrasonic NDT techniques are available for the detection of defects. Linear ultrasonic NDT e.g. by using guided waves is based on mode conversion and reflection of probe waves by a delamination or a defect, provided the defect is open, resulting in an acoustic impedance mismatch. However, in practical applications defects are often ‘closed’ when not under substantial stress.

Moreover, in most nonlinear ultrasonic NDT techniques the lack in differentiation between sources of nonlinearity makes defects undistinguishable from e.g. nonlinearity induced by mechanical contacts.

Here, we aim to detect damage, addressing the practical difficulties of monitoring vibrating structures. Defects were created and detected in aluminum plate-like samples, using PZT transducers to generate and detect probe waves. The presented diagnostic algorithms compare features of the nonlinear relation between the amplitude of the transmission probe wave and the load on the sample with a threshold value, in order to assess the state of the sample. The applications are robust to environmental changes, are based on durable components, while being sensitive to vibrating defects.

Part of the research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement n°212912 and the “NDTonAIR” project (Training Network in Non-Destructive Testing and Structural Health Monitoring of Aircraft structures) under the action: H2020-MSCA-ITN-2016- GRANT 722134.