Identification of fretting fatigue crack propagation mechanisms using acoustic emission

Meriaux, J.; Boinet, Mickaël; Fouvry, S.; Lenain, J. C.

doi:10.1016/j.triboint.2010.06.009

Cited by 57 publications

(37 citation statements)

References 19 publications

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“…Several nondestructive testing (NDT) techniques including ultrasonic testing (UT) [25,26], acoustic emission (AE) [14,25,[27][28][29][30][31][32][33][34][35][36][37][38][39], digital image correlation (DIC) [14,29,38,[40][41][42], and infrared thermography (IRT) [25,28,38], have been used to gain information on the state of materials subjected to monotonic or cyclic loading. Each technique provides its own advantages and disadvantages in the information it can obtain, while in general all techniques could be used to inform models for prediction of fracture at the component and structural levels [25,29,31,33,39].…”

Section: Introductionmentioning

confidence: 99%

“…Known sources of AE include plastic deformation [32,50], cracking [27,34,35,37,38], corrosion [50,51], and others [14,[52][53][54]. In fact, AE monitoring has been used for material characterization and damage identification across length scales including microscale [14,30,32], mesoscale [27,29,34,38,39] and macroscale [25,31], which makes this technique particularly relevant in the recently widely explored area of Integrated Computational Materials Engineering (ICME) [55,56].…”

Section: Introductionmentioning

confidence: 99%

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In Situ Microscopic Investigation to Validate Acoustic Emission Monitoring

et al. 2015

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A novel experimental mechanics technique using Scanning Electron Microscopy (SEM) in conjunction with Acoustic Emission (AE) monitoring is discussed to investigate microstructure-sensitive mechanical behavior and damage of metals and to validate AE related information. Validation for the use of AE method was obtained by using aluminum alloy sharp notch specimens with different geometries tested inside the microscope and compared to results obtained outside the microscope, as well as to previously published data on similar investigations at the laboratory specimen scale. Additionally, load data were correlated with both AE information and microscopic observations of microcracks around grain boundaries as well as secondary cracks, voids, and slip bands. The reported AE results are in excellent agreement with similar findings at the mesoscale, while they are further correlated with in situ and post mortem observations of microstructural damage processes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In Situ Microscopic Investigation to Validate Acoustic Emission Monitoring

et al. 2015

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“…It was found that acoustic emissions generated during different forming processes provide useful information for detecting die wear and cracking, friction properties, state of lubrication, and others. Meriaux [16] studied crack propagation mechanisms in fretting fatigue using acoustic emissions. In addition, AE signals resulting from sliding motion, friction and wear in different mechanical elements were studied.…”

Section: Introductionmentioning

confidence: 99%

Analysis of experimentally generated friction related acoustic emission signals

Asamene

Sundaresan

2012

Wear

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“…K-means has been used by Moevus et al [21] to differentiate the damage mechanism of matrix cracking of SiCf/[Si-B-C] composites that exhibit different tensile behaviours using AE signals. Other research using K-means technique to identify the fretting fatigue crack propagation also has been using the AE approach [22].…”

Section: K-means Clustering Methodsmentioning

confidence: 99%

Fatigue damage monitoring using un-supervised clustering method of acoustic emission signal on SAE 1045 steel

Mohammad

Tajuddin

Abdullah³

et al. 2022

Int. J. Automot. Mech. Eng.

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This paper described the capability of acoustic emission (AE) technique in monitoring the fatigue damage level using unsupervised clustering technique. As fatigue damage is being a major contributing factor to component failure, it is essential to evaluate the level of damage caused by fatigue load in order to prevent the catastrophic failure of the structure. It is a concern in this study to differentiate the AE signals according to the fatigue damage stages by implementing an unsupervised clustering technique. In this study, the AE signals were collected on specimens made of medium carbon steel SAE 1045 that underwent the axial fatigue testing. The test was run at three loading values of 600, 640 and 680 MPa. The pattern behaviour of AE signals was recorded using a piezoelectric sensor in a form of time domain history signal. Later, the AE signals collected were analysed and clustered using K-means technique. Five clusters of K1, K2, K3, K4, and K5 have been found for the specimens subjected to stress value of 600-680 MPa. The optimum numbers of K clusters were determined using the smallest objective function in their group which ranges between 2.6 to 3.0. This pilot investigation shows that it may be useful to estimate the remaining life for a component before it fails.

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Identification of fretting fatigue crack propagation mechanisms using acoustic emission

Cited by 57 publications

References 19 publications

In Situ Microscopic Investigation to Validate Acoustic Emission Monitoring

In Situ Microscopic Investigation to Validate Acoustic Emission Monitoring

Analysis of experimentally generated friction related acoustic emission signals

Fatigue damage monitoring using un-supervised clustering method of acoustic emission signal on SAE 1045 steel

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