Acoustic emission detection of fatigue damage in cruciform welded joints

Yu, Jing; Ziehl, Paul; Matta, Fabio; Pollock, Adrian A.

doi:10.1016/j.jcsr.2013.03.017

Cited by 43 publications

(18 citation statements)

References 11 publications

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“…However, the derived source is the representative acoustic emission; the difference between AE waves due to generalized theory and those of cracking has not been clearly realized. The source mechanism is significantly influenced by the material composition [31][32][33][34][35][36]. For instance, sources associated with composites are matrix cracking, fiber-matrix debonding, fiber pull-out, fiber bridging, inter-ply failure, delamination, and fiber breakage.…”

Section: Acoustic Emission Methodsmentioning

confidence: 99%

Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing

Zhang

Ozevin

Hardman

et al. 2017

Metals

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In many rotating machinery applications, such as helicopters, the splines of an externally-splined steel shaft that emerges from the gearbox engage with the reverse geometry of an internally splined driven shaft for the delivery of power. The splined section of the shaft is a critical and non-redundant element which is prone to cracking due to complex loading conditions. Thus, early detection of flaws is required to prevent catastrophic failures. The acoustic emission (AE) method is a direct way of detecting such active flaws, but its application to detect flaws in a splined shaft in a gearbox is difficult due to the interference of background noise and uncertainty about the effects of the wave propagation path on the received AE signature. Here, to model how AE may detect fault propagation in a hollow cylindrical splined shaft, the splined section is essentially unrolled into a metal plate of the same thickness as the cylinder wall. Spline ridges are cut into this plate, a through-notch is cut perpendicular to the spline to model fatigue crack initiation, and tensile cyclic loading is applied parallel to the spline to propagate the crack. In this paper, the new piezoelectric sensor array is introduced with the purpose of placing them within the gearbox to minimize the wave propagation path. The fatigue crack growth of a notched and flattened gearbox spline component is monitored using a new piezoelectric sensor array and conventional sensors in a laboratory environment with the purpose of developing source models and testing the new sensor performance. The AE data is continuously collected together with strain gauges strategically positioned on the structure. A significant amount of continuous emission due to the plastic deformation accompanied with the crack growth is observed. The frequency spectra of continuous emissions and burst emissions are compared to understand the differences of plastic deformation and sudden crack jump. The correlation of the cumulative AE events at the notch tip and the strain data is used to predict crack growth. The performance of the new sensor array is compared with the conventional AE sensors in terms of signal to noise ratio and the ability to detect fatigue cracking.

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Section: Acoustic Emission Methodsmentioning

confidence: 99%

Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing

Zhang

Ozevin

Hardman

et al. 2017

Metals

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“…As a result, both the accuracy and robustness of crack detection can be improved by using sensing technologies. In the context of distortion-induced fatigue crack detection, Yu et al [9] reported an acoustic emission approach for identifying fatigue damage at the fillet weld in representative cruciform joints of steel bridges; Alavi et al [10] demonstrated a self-powered sensing approach based on a piezo-floating-gate (PFG) sensor for detecting distortion-induced fatigue cracks; and Kong and Li [11] adopted a computer vision-based method to detect distortion-induced fatigue cracks through video feature tracking. An important challenge with these methods is their reliance on extensive human operations to collect critical measurements (acoustic emission data, voltage, or digital videos) in the field, making it challenging to implement long-term continuous crack monitoring of steel bridges.…”

Section: Introductionmentioning

confidence: 99%

Sensing distortion-induced fatigue cracks in steel bridges with capacitive skin sensor arrays

Kong

Collins

et al. 2018

Smart Mater. Struct.

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Distortion-induced fatigue cracks represent the majority of fatigue cracks in steel bridges in the United States. Currently, bridge owners, such as the state departments of transportation (DOTs), rely on human inspection to detect, monitor, and quantify these cracks so that appropriate repairs can be applied before cracks reach critical sizes. However, visual inspections are costly, labor intensive, and may be prone to error due to inconsistent skills among bridge inspectors. In this study, we represent a novel strain-based approach for sensing distortion-induced fatigue cracks in steel bridges using soft elastomeric capacitor (SEC) arrays. Compared with traditional foil strain gauges, the SEC technology is a large-area and flexible skin-type strain sensor that can measure a wide range of strain over a large surface. Previous investigations have verified the suitability of a single SEC for sensing an in-plane fatigue crack in a small-scale steel specimen. In this paper, we further demonstrate the ability of SECs for sensing distortion-induced fatigue cracks. The proposed strategy consists of deploying an array of SECs to cover a large fatigue-susceptible region and establishing a fatigue sensing algorithm by constructing a crack growth index (CGI) map. The effectiveness of the strategy was experimentally validated through fatigue tests of bridge girder to cross-frame connection models with distortion-induced fatigue cracks. Test results verified that by deploying an SEC array, multiple CGIs can be obtained over the fatigue-susceptible region, offering a more comprehensive picture of fatigue damage. Furthermore, by monitoring a series of CGI maps constructed under different fatigue cycles, the fatigue crack growth can be clearly visualized through the intensity change in the CGI maps.

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“…Advanced sensing technologies have attracted great attention in the structural health monitoring (SHM) and nondestructive testing (NDT) communities for detecting and/or monitoring of distortion-induced fatigue cracks in steel bridges. Examples include acoustic emission technology proposed by Yu et al [5] , piezo-floating-gate (PFG)…”

Section: Introductionmentioning

confidence: 99%

Dense capacitive sensor array for monitoring distortion-induced fatigue cracks in steel bridges

Kong

Collins

et al. 2018

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018

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Distortion-induced fatigue cracks caused by differential deflections between adjacent girders are common issues for steel girder bridges built prior to the mid-1980s in the United States. Monitoring these fatigue cracks is essential to ensure bridge structural integrity. Despite various level of success of crack monitoring methods over the past decades, monitoring distortion-induced fatigue cracks is still challenging due to the complex structural joint layout and unpredictable crack propagation paths. Previously, the authors proposed soft elastomeric capacitor (SEC), a large-size flexible capacitive strain sensor, for monitoring in-plane fatigue cracks. The crack growth can be robustly identified by extracting the crack growth index (CGI) from the measured capacitance signals. In this study, the SECs are investigated for monitoring distortion-induced fatigue cracks. A dense array of SECs is proposed to monitor a large structural surface with fatigue-susceptible details. The effectiveness of this strategy has been verified through a fatigue test of a large-scale bridge girder to cross-frame connection model. By extracting CGIs from the SEC arrays, distortion-induced fatigue crack growth can be successfully monitored.

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Acoustic emission detection of fatigue damage in cruciform welded joints

Cited by 43 publications

References 11 publications

Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing

Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing

Sensing distortion-induced fatigue cracks in steel bridges with capacitive skin sensor arrays

Dense capacitive sensor array for monitoring distortion-induced fatigue cracks in steel bridges

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