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https://hdl.handle.net/2440/135625
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Type: | Journal article |
Title: | Low-frequency Lamb wave mixing for fatigue damage evaluation using phase-reversal approach |
Author: | Zhu, H. Ng, C.T. Kotooussov, A. |
Citation: | Ultrasonics, 2022; 124:106768-1-106768-11 |
Publisher: | Elsevier BV |
Issue Date: | 2022 |
ISSN: | 0041-624X 1874-9968 |
Statement of Responsibility: | Hankai Zhu, Ching Tai Ng, Andrei Kotousov |
Abstract: | Fatigue damage is difficult to detect and evaluate non-destructively, specifically at its early stages (before the macro-crack formation). In this study, fatigue damage is evaluated based on the growth rate of the combinational harmonics generated by mixing of two fundamental symmetric mode (S0) of Lamb waves in the low frequency range. The incorporation of the phase reversal approach to the wave mixing method could potentially improve the evaluation of the combinational and second harmonics and avoid the influence of other undesirable har- monics. A series of parametric case studies are carried out using the three-dimensional (3D) finite element (FE) method to investigate the effects of the excitation frequencies and time delay of the incident waves in wave mixing on the transient response of a weakly-nonlinear material. The numerical results and experimental results show that the sum combinational harmonic and second harmonics are sensitive to weak material nonlinearities. Further experiments on damaged samples by cyclic loading demonstrate that the sum combinational harmonic has much better sensitivity to the progressive fatigue damage than the the second harmonics. In general, the outcomes of this study indicate that the damage evaluation of early stage fatigue damage is feasible and effective with the wave mixing method using the S0 waves generated at low frequency, and the phase-reversal approach improves considerably the quality of experimental results in the fatigue damage evaluation. |
Keywords: | Nonlinear Lamb wave mixing Phase-reversal approach Material nonlinearity Fatigue damage Structural health monitoring |
Rights: | © 2022 Elsevier B.V. All rights reserved. |
DOI: | 10.1016/j.ultras.2022.106768 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200102300 http://purl.org/au-research/grants/arc/DP210103307 |
Appears in Collections: | Civil and Environmental Engineering publications |
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