Journal Articles

The Fatigue crack represents one of the most likely reasons for metal structural failures. It is crucial to detect it effectively to avoid a disastrous tragedy. This paper presented a developed procedure to assess the structural integrity and identify the fatigue crack using local vibration techniques. The process includes building computational modeling and conducting experimental investigations to detect and visualize the interesting fatigue crack. The finite-element modal analysis was performed to predict the resonance frequencies which cause high local vibration over the damage domain and extract corresponding mode shapes. These numerical results were validated by conducting electromechanical impedance spectroscopy (EMIS) and the scanning laser Doppler vibrometer (SLDV) experiments based on a developed method. The electromechanical impedance spectroscopy experiment was implemented to predict the crack resonance frequencies by measuring the electro-mechanical impedance of the bonded piezoelectric wafer active sensor (PWAS). Based on the developed method, the bonded PWAS was used to excite the test plate at a high frequency utilizing a linear sine wave chirp from 1 to 500 kHz. The SLDV was used to acquire the dynamic response of each scanning area point. The crack resonance frequencies and corresponding operational vibration shapes were extracted from SLDV measurements to detect and visualize the interesting fatigue crack. The result showed the frequency spectrum of SLDV measurements and corresponding operation vibration shapes have much information about the tested crack compared with the E/M impedance spectrum. Gathering the experimental and finite element modeling results shows good potential for accurately assessing structural damage.

Acoustic emission (AE) was monitored during stress intensity factor (SIF)-controlled highcycle fatigue (HCF) tests on an aluminum 2024-T3 specimen with a fatigue crack growing at its center. The SIF control was implemented in such a manner that crack growth could be slowed down and even inhibited while the fatigue experiment continued. In the beginning, a specific type of AE signal was observed while the crack was allowed to grow to up to approximately 9.4 mm in length. Subsequently, the load was reduced in order to control the SIF value at the crack tip and to inhibit the crack growth. AE signals were recorded even when the crack stopped growing, although the specific signature of these AE signals was different from those observed when the crack was growing, as discussed in the text. The gist of the phenomenon reported in this article is that strong AE signals could still be observed even when the crack stopped growing. These latter AE signals could be due to rubbing and clapping of the crack faying surfaces. Travel analysis was consistently performed to ensure that these AE signals were originating from the crack, though not necessarily from the crack tip. In addition, absorbing clay wave dams were built around the crack region to inhibit boundary reflections and grip noise. Fast Fourier Transform (FFT) and Choi–Williams Transform (CWT) analysis were performed to classify the AE signals. It was observed that the AE signals related to crack growth were clearly different from the AE signals originating from the crack while the crack was not growing. Strong S0-mode Lamb wave components were observed in the crack-growth AE signals, whereas strong A0-mode Lamb wave components dominated the non-crack-growth AE signals. Pearson correlation clustering analysis was performed to compare the crack-growth and non-crack growth AE signals. We propose that the fatigue-crack faying surfaces may undergo rubbing and/or clapping during fatigue cyclic loading and thus produce strong AE signals that are registered by the AE system as hits, although the crack is not actually growing. The understanding of this phenomenon is very important for the design of the structural health monitoring (SHM) system based on AE-hit signal capture and interpretation.

Acoustic waves are widely used in structural health monitoring (SHM) for detecting fatigue cracking. The strain energy released when a fatigue crack advances has the effect of exciting acoustic waves, which travel through the structures and are picked up by the sensors. Piezoelectric wafer active sensors (PWAS) can effectively sense acoustic waves due to fatigue-crack growth. Conventional acoustic-wave passive SHM, which relies on counting the number of acoustic events, cannot precisely estimate the crack length. In the present research, a novel method for estimating the crack length was proposed based on the high-frequency resonances excited in the crack by the energy released when a crack advances. In this method, a PWAS sensor was used to sense the acoustic wave signal and predict the length of the crack that generated the acoustic event. First, FEM analysis was undertaken of acoustic waves generated due to a fatigue-crack growth event on an aluminum-2024 plate. The FEM analysis was used to predict the wave propagation pattern and the acoustic signal received by the PWAS mounted at a distance of 25 mm from the crack. The analysis was carried out for crack lengths of 4 and 8 mm. The presence of the crack produced scattering of the waves generated at the crack tip; this phenomenon was observable in the wave propagation pattern and in the acoustic signals recorded at the PWAS. A study of the signal frequency spectrum revealed peaks and valleys in the spectrum that changed in frequency and amplitude as the crack length was changed from 4 to 8 mm. The number of peaks and valleys was observed to increase as the crack length increased. We suggest this peak–valley pattern in the signal frequency spectrum can be used to determine the crack length from the acoustic signal alone. An experimental investigation was performed to record the acoustic signals in crack lengths of 4 and 8 mm, and the results were found to match well with the FEM predictions.

The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal analysis technique via AI. A finite element model (FEM) study was first performed to understand the effects of fatigue crack length on the resulting AE waveforms and a fatigue experiment was performed to capture experimental AE waveforms. Finally, this database of experimental AE waveforms was used with a convolutional neural network to build a system capable of performing automated classification and prediction of the length of a fatigue crack that excited respective AE signals. AE signals captured during a fatigue crack growth experiment were found to match closely with the FEM simulations. This novel AI system proved to be effective at predicting the crack length of an AE signal at an accuracy of 98.4%. This novel AI-enabled AE signal analysis technique will provide a crucial step forward in the development of a comprehensive structural health monitoring (SHM) system.

In this paper, the non-destructive testing (NDT), structural health monitoring (SHM), and scanning laser Doppler vibrometer (SLDV) techniques were presented to quantify three simulated delaminations inserted at different depths of a unidirectional composite plate. First, the RollerFORM and Omniscan equipment were sufficiently used to identify the delaminations. Second, in conjunction with guided waves, the developed imaging method was successfully used to detect and quantify the interested delaminations. The tuning curves were determined experimentally to define the dominant Lamb wave modes of incident waves. Third, multi-physics three-dimensional finite element simulations of propagating and interacting Lamb waves with delaminations were implemented to extract the wavefield data for wavenumber analysis. The experimental part was conducted to validate the numerical results using SLDV. The effect of the delamination depth on the trapped waves generated over the delamination region was studied numerically and experimentally. The results showed that trapped waves could be affected by the delamination depth. Both numerical and experimental results demonstrated that the near surface delamination has strong trapped waves over the delamination region while the far surface delamination has weak trapped waves. The energy distribution maps of numerical and experimental wavefields data sufficiently quantified the interested delaminations. A good agreement was achieved between the numerical and experimental results.

Barely visible impact damage (BVID) due to low velocity impact events in composite aircraft structures are becoming prevalent. BVID can have an adverse effect on the strength and safety of the structure. During aircraft inspections it can be extremely difficult to visually detect BVID. Moreover, it is also a challenge to ascertain if the BVID has in-fact caused internal damage to the structure or not. This paper describes a method to ascertain whether or not internal damage happened during the impact event by analyzing the high-frequency information contained in the recorded acoustic emission signal signature. Multiple 2 mm quasi-isotropic carbon fiber reinforced polymer (CFRP) composite coupons were impacted using the ASTM D7136 standard in a drop weight impact testing machine to determine the mass, height and energy parameters to obtain approximately 1” impact damage size in the coupons iteratively. For subsequent impact tests, four piezoelectric wafer active sensors (PWAS) were bonded at specific locations on each coupon to record the acoustic emission (AE) signals during the impact event using the MISTRAS micro-II digital AE system. Impact tests were conducted on these instrumented 2 mm coupons using previously calculated energies that would create either no damage or 1” impact damage in the coupons. The obtained AE waveforms and their frequency spectrums were analyzed to distinguish between different AE signatures. From the analysis of the recorded AE signals, it was verified if the structure had indeed been damaged due to the impact event or not. Using our proposed structural health monitoring technique, it could be possible to rapidly identify impact events that cause damage to the structure in real-time and distinguish them from impact events that do not cause damage to the structure. An invention disclosure describing our acoustic emission structural health monitoring technique has been filed and is in the process of becoming a provisional patent.

This paper presents a new technique for the extraction of high-order wave-damage interaction coefficients (WDIC) through modal decomposition. The frequency and direction dependent complex-valued WDIC are used to model the scattering and mode conversion phenomena of guided wave interaction with damage. These coefficients are extracted from the harmonic analysis of local finite element model (FEM) mesh with non-reflective boundaries (NRB) and they are capable of describing the amplitude and phase of the scattered waves as a function of frequency and direction. To extract the WDIC of each wave mode, all the possible propagating wave modes are considered to be scattered simultaneously from the damage and propagate independently. Formulated in frequency domain, the proposed method is highly efficient, providing an overdetermined equation system for the calculation of mode participation factors, i.e., WDIC of each mode. Case studies in a 6-mm aluminum plate were carried out to validate the WDIC of: (1) a through-thickness hole and (2) a sub-surface crack. At higher frequency, scattered waves of high-order modes will appear and their WDIC can be successfully extracted through the modal decomposition.

Acoustic waves are widely used in structural health monitoring (SHM) for detecting fatigue cracking. The strain energy released when a fatigue crack advances has the effect of exciting acoustic waves, which travel through the structures and are picked up by the sensors. Piezoelectric wafer active sensors (PWAS) can effectively sense acoustic waves due to fatigue-crack growth. Conventional acoustic-wave passive SHM, which relies on counting the number of acoustic events, cannot precisely estimate the crack length. In the present research, a novel method for estimating the crack length was proposed based on the high-frequency resonances excited in the crack by the energy released when a crack advances. In this method, a PWAS sensor was used to sense the acoustic wave signal and predict the length of the crack that generated the acoustic event. First, FEM analysis was undertaken of acoustic waves generated due to a fatigue-crack growth event on an aluminum-2024 plate. The FEM analysis was used to predict the wave propagation pattern and the acoustic signal received by the PWAS mounted at a distance of 25 mm from the crack. The analysis was carried out for crack lengths of 4 and 8 mm. The presence of the crack produced scattering of the waves generated at the crack tip; this phenomenon was observable in the wave propagation pattern and in the acoustic signals recorded at the PWAS. A study of the signal frequency spectrum revealed peaks and valleys in the spectrum that changed in frequency and amplitude as the crack length was changed from 4 to 8 mm. The number of peaks and valleys was observed to increase as the crack length increased. We suggest this peak–valley pattern in the signal frequency spectrum can be used to determine the crack length from the acoustic signal alone. An experimental investigation was performed to record the acoustic signals in crack lengths of 4 and 8 mm, and the results were found to match well with the FEM predictions.

This paper presents a new methodology for detecting various types of composite damage, such as delamination and impact damage, through the application of multimode guided waves. The basic idea is that various wave modes have different interactions with various types of composite damage. Using this method, selective excitations of pure-mode guided waves were achieved using adjustable angle beam transducers (ABTs). The tuning angles of various wave modes were calculated using Snell’s law applied to the theoretical dispersion curves of composite plates. Pitch–catch experiments were conducted on a 2-mm quasi-isotropic carbon fiber-reinforced polymer (CFRP) composite plate to validate the excitations of pure fundamental symmetric mode (S0) and shear horizontal mode (SH0). The generated pure S0 mode and SH0 mode were used to detect and separate the simulated delamination and actual impact damage. It was observed that S0 mode was only sensitive to the impact damage, while SH0 mode was sensitive to both simulated delamination and impact damage. The use of pure S0 and SH0 modes allowed for damage separation. In addition, the proposed method was applied to a 3-mm-thick quasi-isotropic CFRP composite plate using multimode guided wave detection to distinguish between delamination and impact damage. The experimental results demonstrated that the proposed method has a good capability to detect and separate various damage types in composite structures.

This paper proposes a new single-mode guided wave-based method to detect various types of damage in aerospace composites. In this method, single-mode guided wave excitation was achieved using adjustable angle beam transducers (ABT). The ABT tuning angles of various pure-mode guided waves were calculated based on Snell's law applied to the composite dispersion curves. A finite element (FE) simulation of pure S0 mode excitation in a crossply composite plate was conducted and the simulation results were validated by the experiment. For the first time, angle beam transducers were applied to generate pure shear horizontal (SH0) wave in a thick quasi-isotropic composite plate. The pure SH0 wave excitation was successfully verified by a three-dimensional (3D) FE simulation. SH0-mode wave propagation and interaction with delaminations were further conducted and strong trapped waves within the delamination regions were observed. Experiments using S0 or SH0 pure-mode guided waves were conducted to detect various types of composite damage, such as wrinkle damage in the crossply composite plate, multilayer delaminations by Teflon inserts, and actual impact damage in the thick quasi-isotropic composite plate. A significant amplitude drop was observed due to the presence of different composite damage types. In addition, a linear scanning method using pure SH0 wave was also developed to estimate the sizes of delaminations and impact damage. Both numerical and experimental results demonstrated the validity and usefulness of the proposed method for the detection of various damage types in composites.