Mr. Ming Hong Profile

Ming Hong

at Hong Kong Polytechnic Univ

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Proceedings Article | 23 March 2015 Paper

Uncertainty quantification of relative acoustic nonlinearity parameter of guided waves for damage detection in composite structures

Ming Hong, Zhu Mao, Michael Todd, Zhongqing Su, Xinlin Qing

Proceedings Volume 9438, 94380A (2015) https://doi.org/10.1117/12.2084182

KEYWORDS: Composites, Waveguides, Damage detection, Acoustics, Statistical analysis, Carbon, Signal processing, Second-harmonic generation, Wave propagation, Semiconducting wafers

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Nonlinear guided waves have been studied extensively for the characterization of micro-damage in plate-like structures, such as early-stage fatigue and thermal degradation in metals. Meanwhile, an increasing number of studies have reported the use of nonlinear acoustic techniques for detection of impact damage, fatigue, and thermal fatigue in composite structures. Among these techniques, the (relative) acoustic nonlinearity parameter, extracted from acousto-ultrasonic waves based on second-harmonic generation, has been considered one of the most popular tools for quantifying the detection of nonlinearity in inspected structures. Considering the complex nature of nonlinearities involved in composite materials (even under healthy conditions), and operational/environmental variability and measurement noise, the calculation of the relative acoustic nonlinearity parameter (RANP) from experimental data may suffer from considerable uncertainties, which may impair the quality of damage detection. In this study, we aim to quantify the uncertainty of the magnitude of the RANP estimator in the context of impact damage identification in unidirectional carbon fiber laminates. First, the principles of nonlinear ultrasonics are revisited briefly. A general probability density function of the RANP is then obtained through numerical evaluation in a theoretical setting. Using piezoelectric wavers, continuous sine waves are generated in the sample. Steady-state responses are acquired and processed to produce histograms of the RANP estimates before and after the impact damage. These observed histograms are consistent with the predicted distributions, and examination of the distributions demonstrates the significance of uncertainty quantification when using the RANP for damage detection in composite structures.

Proceedings Article | 9 March 2014 Paper

Fatigue damage localization using time-domain features extracted from nonlinear Lamb waves

Ming Hong, Zhongqing Su, Ye Lu, Li Cheng

Proceedings Volume 9064, 906405 (2014) https://doi.org/10.1117/12.2044031

KEYWORDS: Aluminum, Sensor networks, Nonlinear optics, Signal processing, Diagnostics, Waveguides, Sensors, Transducers, Image fusion, Semiconducting wafers

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Nonlinear guided waves are sensitive to small-scale fatigue damage that may hardly be identified by traditional techniques. A characterization method for fatigue damage is established based on nonlinear Lamb waves in conjunction with the use of a piezoelectric sensor network. Theories on nonlinear Lamb waves for damage detection are first introduced briefly. Then, the ineffectiveness of using pure frequency-domain information of nonlinear wave signals for locating damage is discussed. With a revisit to traditional gross-damage localization techniques based on the time of flight, the idea of using temporal signal features of nonlinear Lamb waves to locate fatigue damage is introduced. This process involves a time-frequency analysis that enables the damage-induced nonlinear signal features, which are either undiscernible in the original time history or uninformative in the frequency spectrum, to be revealed. Subsequently, a finite element modeling technique is employed, accounting for various sources of nonlinearities in a fatigued medium. A piezoelectric sensor network is configured to actively generate and acquire probing Lamb waves that involve damageinduced nonlinear features. A probability-based diagnostic imaging algorithm is further proposed, presenting results in diagnostic images intuitively. The approach is experimentally verified on a fatigue-damaged aluminum plate, showing reasonably good accuracy. Compared to existing nonlinear ultrasonics-based inspection techniques, this approach uses a permanently attached sensor network that well accommodates automated online health monitoring; more significantly, it utilizes time-domain information of higher-order harmonics from time-frequency analysis, and demonstrates a great potential for quantitative characterization of small-scale damage with improved localization accuracy.

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