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Acoustic Emission - Crossref

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Last Updated: 03 December 2022

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Cluster analysis on damage pattern recognition in carbon/epoxy composites using acoustic emission wavelet packet

Acoustic Emission technique's aim is to investigate damage pattern recognition in carbon/epoxy composite laminates. Decoding patterns of these signals with disputed classification results was used to analyze the clustering center to find the frequency band with relatively high energy corresponding each damage mode. It can be tested well, and the k-means algorithm does better in composite recognition of composites than it does.

Source link: https://doi.org/10.1177/07316844221144336


An Integrated Approach to Real-Time Acoustic Emission Damage Source Localization in Piled Raft Foundations

While AE source localization tools that can determine the damage location in a piled-raft foundation are impractical due to the layered-raft foundation's complicated geometry, the PRF is a common deep foundation material for high-rise buildings. We recommend an integrated approach to localize AE sources in the PRF by using the updated Akaike information criterion method and testing its reliability to map with pile zones. The results show that the combined effort of the modified AIC method and the Simplex technique can localize the AE source zones with high accuracy, greater than 85% on average. The two-stage AIC picker can be used for automated real-time AE monitoring to detect crack generation and its location in buried foundations that are impossible to be examined visually.

Source link: https://doi.org/10.3390/app10238727


A machine learning framework for damage mechanism identification from acoustic emissions in unidirectional SiC/SiC composites

Abstract In this work, we show that damage mechanism detection from acoustic emission signals generated in minicomposites with elastically similar constituents is possible. Despite the identical constituent elastic properties of the matrix and fiber, similar constituent elastic characteristics of the matrix and fiber were discovered based on the frequency information contained in the AE event. Even though the effort is purely domain-knowledge agnostic, the resultant identification of AE events closely followed CMC damage chronology, wherein early matrix cracking is immediately followed by fiber cracking.

Source link: https://doi.org/10.1038/s41524-021-00620-7


Damage mechanism identification in composites via machine learning and acoustic emission

For a comprehensive analysis of damage evolution, recent advancements in machine learning have allowed us to determine the waveform-damage relationship in higher-dimensional environments. Any machine learning framework's fundamentals for damage mechanism characterization are addressed, including those that are currently in use, under construction, and yet to be explored.

Source link: https://doi.org/10.1038/s41524-021-00565-x


An Algorithm of Acoustic Emission Location for Complex Composite Structure

Acoustic emission is often used in engineering and rock mechanics. Based on an optimized shortest path algorithm, a new travel time estimation scheme suitable for CCS with step-like velocity change is suggested in this paper. The new algorithm can accurately estimate the travel time and ray path of the sample, according to the AE location after the failure of a simulated CCS in the laboratory.

Source link: https://doi.org/10.3390/app122312323


Structural control within flawed rock specimens under external loading as visualized through repeating nucleation on multiple sites by acoustic emission (AE)

Detailed review of discontinuities in rock mass has been ongoing for decades by a variety of laboratory analogues in poor rock samples, using the artificially prepared flaw as the analogue of rock mass discontinuity. The role of macroscale flaw as the primary system has been generally ignored. On the pre-flawed specimens under loading, we perform exhaustive investigation of the acoustic emission from the rock fracture process. Artificial flaw play a non-negligible part as a governing system for the upcoming rock fracture process, which also includes a sequence of nucleation point alteration via stress transfer. The scale-invariance characteristic is shown to be fundamentally different within specimens under SLS and/or IP surveillance, indicating the lack of specimen-scale for IP control. Therefore, no u2018fracture coalescenceu2019 zone can be investigated separately without considering its external rock fracture evolution and determining scale-invariance.

Source link: https://doi.org/10.1093/gji/ggac470


Mechanical properties and acoustic emission characteristics of deep hard coal after segmented high-temperature treatment

Temperature at 100 percent in a deep hard coal sample was not sufficient to change the main ingredients of the experiment, including compression strength, elastic modulus, and acoustic emission behavior of coal samples, according to test findings. However, with the change of contact surface position between heating section and non-heating section, the heating section of the coal sample made a shear failure surface slightly rise, but separation failure surface improved with the change of contact surface location between heating section and non-heating section. After heating treatment at 200,u2103, acoustic emission parameter for coal sample was debated and confirmed by experimentation of acoustic emission parameter for the strength of coal sample, based on variation of the sample's heating characteristics.

Source link: https://doi.org/10.21203/rs.3.rs-2261416/v1


Classification of Located Acoustic Emission Events Using Neural Network

Abstract The location of acoustic emission events is one of the key evaluation techniques in AE analysis. During six hydraulic fracturing experiments in massive salt rock, the aim of this research is the use of a neural network to classify clustered AE events. For input data, the longitudinal and transversal elastic waves' signal arrival time profiles were used to prepare, to measure, and to evaluate the neural network. In total, 765 AE events were classified in various target groups. For analyzing the results of the neural network approach, receiver operating characteristic analysis was used. Clustered events were classified correctly by the neural network, though few events outside of a cluster's geographic region could not be matched to any cluster. The results were robust, showing the high success of Deep Learning techniques in the location of AE events, according to Bootstrap's review.

Source link: https://doi.org/10.1007/s10921-022-00913-x


On the acoustic emission characteristics of airfoils with different trailing edge configurations

The present research investigates the flow through flat plates and NACA0012 airfoils with the various trailing edge and both end-rounded designs in order to determine the correct edge geometry for low noise, according to the present study. Although realistic and flat plate airfoils have lower acoustic emissions than conventional and flat plate airfoils, the realistic airfoils radiate lower acoustic emissions as compared to flat plate airfoils, a common feature of downstream directivity. Both end-rounded trailing edged foils' lower far-field acoustic emissions are owing to the increase in the boundary layer characteristics, as well as reduced vortex strength as compared to other foil geometries.

Source link: https://doi.org/10.1177/09544100221141313

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions