This paper focuses on using two developed imaging methods to localize the impact points on a composite plate. The first developed imaging method (Method 1) was firstly investigated on a metallic plate. A network of nine piezoelectric wafer active sensors (PWAS) was instrumented on an aluminum plate to receive impact signals. Based on Method 1, we need at least three sensors, which can be used to determine two hyperbolic paths, to localize an impact point on a metalic plate with known group velocities of generated waves (known its material properties). The observed results indicate that Method 1 can be used successfully to localize the impact points on a metallic plate. These successful results motivated us to investigate Method 1 on a composite plate. A second experiment of impact localization was implemented on a composite plate. Two clusters of sensors (every cluster has three PWAS transducers) were instrumented on the composite specimen to receive the generated acoustic waves due to break pencil leads at different points. The received signals were anaylized using a wavelet transform to determine the time of flight. The group velocity profile of antisymmetric Lamb wave mode was determined analytically at certain frequency. Based on Method 1, two hyperbolic paths, which are generated by four sensors, can be used successfully to localize the impact points on a composite plate with known its material properties. The second developed imaging method (Method 2) was investigated on the same composite specimen with assumption of unknown its material properties (unknown group velocity profile of generated wave). Based on Method 2, we need six sensors distributed on two clusters to determine two straight line paths. The intersection point of these two lines represents the impact point. The results showed that Method 2 can successfully localize the impact points on a composite plate.
Crack rubbing or clapping in metallic structures generates acoustic emission (AE) signals. Such AE signals need to be distinguished from AE signal due to fatigue crack growth event. AE signal due to crack rubbing or clapping of fatigue generated crack was studied for a plate specimen. 20 mm fatigue crack was generated in a 1 mm thick aluminum plate specimen. Vibration-induced excitation was performed on the specimen to induce crack faying surface-motion for AE signal generation. Various specimen resonances have different crack faying surface motions, which were studied from FEM analysis. Modeshapes and crack faying surface motions of the specimen are studied at 35 Hz and 180 Hz specimen resonances. AE signals at various specimen resonances were recorded by piezoelectric wafer active sensors (PWAS) and the recorded waveforms are analyzed to obtain AE signatures. At various specimen resonances, AE signals have different signatures due to the change in crack faying surface motions. AE recording was done by using multiple PWAS sensors placed at various distances from the crack. The difference in AE signals close to crack and distant from crack as well as the geometric spreading of AE signals originating due to crack rubbing was studied from multi-sensor experiments.
Structural health monitoring (SHM) and nondestructive evaluation (NDE) deals with the nondestructive inspection of defects, corrosion, leaks in engineering structures by using ultrasonic guided waves. In the past, simplistic structures were often considered for analyzing the guided wave interaction with the defects. In this study, we focused on more realistic and relatively complicated structure for detecting any defect by using a non-contact sensing approach. A plate with a stiffener was considered for analyzing the guided wave interactions. Piezoelectric wafer active transducers were used to produce excitation in the structures. The excitation generated the multimodal guided waves (aka Lamb waves) that propagate in the plate with stiffener. The presence of stiffener in the plate generated scattered waves. The direct wave and the additional scattered waves from the stiffener were experimentally recorded and studied. These waves were considered as a pristine case in this research. A fine horizontal semi-circular crack was manufactured by using electric discharge machining in the same stiffener. The presence of crack in the stiffener produces additional scattered waves as well as trapped waves. These scattered waves and trapped wave modes from the cracked stiffener were experimentally measured by using a scanning laser Doppler vibrometer (SLDV). These waves were analyzed and compared with that from the pristine case. The analyses suggested that both size and shape of the horizontal crack may be predicted from the pattern of the scattered waves. Different features (reflection, transmission, and mode-conversion) of the scattered wave signals are analyzed. We found direct transmission feature for incident A0 wave mode and modeconversion feature for incident S0 mode are most suitable for detecting the crack in the stiffener. The reflection feature may give a better idea of sizing the crack.
Acoustic emission is a widely used and efficient method for structural damage monitoring. Analytical modeling of wave propagation due to dislocations by considering the source as a self-equilibrating moment tensor is a commonly used approach is seismology. In this paper the acoustic emission source definition using moment tensor approach is studied and tried to implement it in thin plates with micro crack acoustic emission source. Depending upon the characteristic of micro crack formed the moment tensor excitation also changes. A study has done to identify the moment tensor components for mod 1, mode 2 or mode 3 like fracture micro crack formation from the classical definition of moment tensor.
Acoustic emission (AE) monitoring technique is a well-known approach in the field of NDE/SHM. AE monitoring from the defect formation and failure in the materials were well studied by the researchers. However, conventional AE monitoring techniques are predominantly based on statistical analysis. In this study we focus on understanding the AE waveforms from the fatigue crack growth using physics based approach. The growth of the fatigue crack causes the acoustic emission in the material that propagates in the structure. One of the main challenges of this approach is to develop the physics based understanding of the AE source itself. The acoustic emission happens not only from the crack growth but also from the interaction of the crack lips during fatigue loading of the materials. As the waveforms are generated from the AE event, they propagate and create local vibration modes along the crack faces. Fatigue experiments were performed to generate the fatigue cracks. Several test specimens were used in the fatigue experiments and corresponding AE waveforms were captured. The AE waveforms were analyzed and distinguished into different groups based on the similar nature on both time domain and frequency domain. The experimental results are explained based on the physical observation of the specimen.
Ultrasonic inspection of multiple-rivet-hole lap joint cracks has been introduced using combined analytical and finite element approach (CAFA). Finite element analyses have been performed on local damage area in spite of the whole large structure and transfer function based analytical model is used to analyze the full structure. “Scattered cube” of complex valued wave damage interaction coefficient (WDIC) that involves scattering and mode conversion of Lamb waves around the damage is used as coupling between analytical and FEM simulation. WDIC is captured for multiple angles of incident Lamb mode (S0 and A0) over the frequency domain to analyze the cracks of multiple-rivet-hole lap joint. By analyzing the scattered cube of WDICs over the frequency domain and azimuthal angles the optimum parameters can be determined for each angle of incidence and the most sensitive signals are obtained using WaveformRevealer2D (WFR2D). These sensitive signals confirm the detection of the butterfly cracks in rivet holes through the installment of the transmitting and sensing PWASs in the proper locations and selecting the right frequency of excitation.
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