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, the authors report on the acoustic emission monitoring during laboratory hydraulic fracture studies conducted on Lyons sandstone samples under different applied external stress, and they compute AE hypocenter locations, analyze event frequency content and compute focal mechanisms (FMS).
102 citations
Journal Article•
TL;DR: In this paper, the authors developed a special cell design which allows them to monitor acoustic emissions stemming from mechanical events in the cell and allow for detailed structural analysis using X-ray diffraction with an internal standard.
Abstract: Silicon is a promising anode material for lithium-ion battery application due to its high specific capacity, low cost, and abundance. However, when silicon is lithiated at room temperature, it can undergo a volume expansion in excess of 280%, which leads to an extensive fracturing. This is thought to be a primary cause of the rapid decay in cell capacity routinely observed. We have developed a special cell design which allows us to monitor acoustic emissions stemming from mechanical events in the cell and allow for detailed structural analysis using X-ray diffraction with an internal standard. The combined result from acoustic emissions and X-ray diffraction allow for a first of its kind detailed look at how silicon anodes degrade and together with presented theories of fracture mechanics enable a material engineering approach to optimize its long term behavior. In collaboration with Kevin Rhodes and Sergiy Kalnaus.
102 citations
01 May 2018
TL;DR: Two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners and a stack of autoencoders and a convolutional neural network are introduced.
Abstract: This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.
102 citations
TL;DR: In this article, a four-point probe method was applied for carbon nanotube (CNT)/epoxy composites using four point probe method with their contents, and the fracture of carbon fiber was detected by nondestructive acoustic emission (AE) relating to electrical resistivity under double-matrix composites (DMC) test.
101 citations
TL;DR: In this article, the authors investigated the applicability of acoustic emission (AE) technique to detect and locate the corrosion-induced failure of high-strength steel tendons of prestressed concrete structures.
101 citations