Acoustic emission from vibration of cracked sheet-metal samples

Joseph, Roshan; Bhuiyan, Yeasin; Giurgiutiu, Victor

doi:10.1016/j.engfracmech.2019.106544

Cited by 18 publications

(9 citation statements)

References 16 publications

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“…However, the FBG sensor performance is affected by the sensor’s directional sensitivity [ 34 , 35 , 36 ]. Piezoelectric wafer active sensors (PWAS) are commonly used to detect AE signals [ 37 , 38 ]. PWAS transducers have excellent characteristics, such as high sensitivity and good stability [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

Joseph

Mei

Migot

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

Joseph

Mei

Migot

et al. 2021

Sensors

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“…Thus, the AE method has a real and significant application in real-time structural health monitoring. When using AE to monitor fatigue crack growth, the possible AE sources include the formation of a new fracture surface and the rubbing or clapping of interface crack surface [21,22].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission of Metallic Specimen with Surface Defect During Fatigue Crack Growth

Rose

Davies

Vien

et al. 2021

Materials Research Proceedings

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Abstract. Acoustic emission is defined as the phenomena whereby transient elastic waves are generated by the rapid release of localized sources within a material. During fatigue crack growth, the formation of new crack surfaces is associated with a sudden release of energy, which constitutes acoustic sources for acoustic emission. This paper investigates the acoustic emission signature arising from fatigue test of a metallic specimen under tensile fatigue test. In this experimental study, dog-bone aluminium alloy specimen with a surface defect was fatigued to failure. It is found that the acoustic emission characteristics are different during the propagation of surface crack, because the source is changing. The results provide a useful guide in identifying source origin based on the characteristics of the acoustic emission waveform.

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“…In this research, under a slow frequency of fatigue loading (<0.25 Hz), for a short advancement of crack length, the AE signals were recorded, and eight signal signatures were discovered related to crack growth and crack rubbing and clapping. AE signals during fatigue crack rubbing/clapping [ 43 ] and vibration-induced fatigue crack rubbing/clapping were also studied experimentally and analytically [ 44 ]. The correlation between acoustic emission count rates and crack propagation rates was studied by Roberts and Talebzadeh [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals

Joseph

Giurgiutiu

2020

Sensors

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The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb wave theory and using this understanding for predictive modeling of AE signals. After a brief introduction, the paper discusses the principles of sensing acoustic signals by using PWAS. The derivation of a closed-form expression for PWAS response due to a stress wave is presented. The transformations happening to the AE signal according to the instrumentations we used for the fatigue crack AE experiment is also discussed. It is followed by a summary of the in situ AE experiments performed for recording fatigue crack growth AE and the results. Then, we present an analytical model of fatigue crack growth AE and a comparison with experimental results. The fatigue crack growth AE source was modeled analytically using the dipole moment concept. By using the source modeling concept, the analytical predictive modeling and simulation of the AE were performed using normal mode expansion (NME). The simulation results showed good agreement with experimental results. A strong presence of nondispersive S0 Lamb wave mode due to the fatigue crack growth event was observed in the simulation and experiment. Finally, the analytical method was verified using the finite element method. The paper ends with a summary and conclusions; suggestions for further work are also presented.

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Acoustic emission from vibration of cracked sheet-metal samples

Cited by 18 publications

References 16 publications

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

Crack-Length Estimation for Structural Health Monitoring Using the High-Frequency Resonances Excited by the Energy Release during Fatigue-Crack Growth

Acoustic Emission of Metallic Specimen with Surface Defect During Fatigue Crack Growth

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals

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