Topic
Acoustic emission
About: Acoustic emission is a research topic. Over the lifetime, 16293 publications have been published within this topic receiving 211456 citations.
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TL;DR: In this paper, acoustic emission descriptors were introduced and respective engineering models, estimating residual strength, were developed to assess static shear strength degradation, due to fatigue, in unidirectional fiber-reinforced composites.
57 citations
TL;DR: In this paper, an inversion processing of acoustic emission (AE) signals was used to determine the critical cracking or delamination events among the AE signals, and the damage progressions in the TBCs were elucidated by correlating the fracture source parameters to the strain curves in time domain.
Abstract: Plasma-sprayed thermal barrier coatings (TBCs), as-sprayed and pre-oxidized, were tested under four-point tensile bending conditions, and their acoustic emission (AE) responses were monitored by an advanced AE system. On the basis of an inversion processing of AE signals, the damage sources in the deposits were localized, identified and classified into three main cracking modes. Furthermore, cracking source parameters (i.e. rise time and crack volume) were estimated and used to determine the critical cracking or delamination events among the AE signals. Consequently, the damage progressions in the TBCs were elucidated by correlating the fracture source parameters to the strain curves in time domain. In the bending tests, vertical cracks were induced in the ceramic top layer at a low strain rate and delamination at the bond/top coat interface accounted for the spallation of top coat before failure. Pre-oxidized samples tended to crack early at a low tensile strain, and the AE sources were characterized by predominant shear fracture (Mode II) at the bond/top coat interface, corresponding to a degradation mechanism of microcracking before failure.
57 citations
TL;DR: In this article, the problem of automatic classification of acoustic emission signals using techniques derived from pattern recognition is addressed, where data were taken from laboratory experimental work on a box girder of a bridge in which the acoustic emission (AE) generation mechanism and location were monitored.
Abstract: The problem of automatic classification of acoustic emission signals using techniques derived from pattern recognition is addressed in this paper. The data were taken from laboratory experimental work on a box girder of a bridge in which the acoustic emission (AE) generation mechanism and location were monitored. Two statistical methods and a neural network procedure have been used to classify the data into groups representing different AE generation mechanisms. The classifiers are constructed using the traditional AE features – four parameters from each burst. Principal component analysis is used to reduce the dimension of the AE data feature vectors to two dimensions, resulting in simple visualisations of the data.
57 citations
TL;DR: In this article, the authors presented the first step towards using multiple acoustic emission (AE) sensors to produce spatially located time series signals for a running engine, which is meant the decomposition of a multi-source signal by acquiring it with an array of sensors and using source location to reconstitute the individual time series attributable to some or all of these signals.
57 citations
TL;DR: In this article, the influence of carbon nanofibers (CNF) and/or piezoelectric (PZT) particles on the fracture behavior of carbon fiber reinforced polymer laminates was investigated.
57 citations