Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material

1994 ◽  
Author(s):  
Urquidi-Macdonald ◽  
Tittmann ◽  
Koopman
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Composite Material ◽  
Artificial Neural Networks ◽  
Carbon Composite ◽  
Carbon Composite Material ◽  
Artificial Neural

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

scholarly journals Identification of the Degree of Degradation of Fibre-Cement Boards Exposed to Fire by Means of the Acoustic Emission Method and Artificial Neural Networks

Materials ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Krzysztof Schabowicz ◽  
Tomasz Gorzelańczyk ◽  
Mateusz Szymków
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Bending Strength ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Fibre Cement ◽  
Artificial Neural ◽  
Degree Of Degradation

This paper presents the results of research aimed at identifying the degree of degradation of fibre-cement boards exposed to fire. The fibre-cement board samples were initially exposed to fire at various durations in the range of 1–15 min. The samples were then subjected to three-point bending and were investigated using the acoustic emission method. Artificial neural networks (ANNs) were employed to analyse the results yielded by the acoustic emission method. Fire was found to have a degrading effect on the fibres contained in the boards. As the length of exposure to fire increased, the fibres underwent gradual degradation, which was reflected in a decrease in the number of acoustic emission (AE) events recognised by the artificial neural networks as accompanying the breaking of the fibres during the three-point bending of the sample. It was shown that it is not sufficient to determine the degree of degradation of fibre-cement boards solely on the basis of bending strength (MOR).

scholarly journals Crack Propagation Analysis Using Acoustic Emission Sensors for Structural Health Monitoring Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Zachary Kral ◽  
Walter Horn ◽  
James Steck
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Aerospace Systems ◽  
Acoustic Emission Sensors ◽  
Artificial Neural ◽  
The Difference

Aerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. As the loading increased, designed cracks extended in length, releasing strain waves in the process. Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). Several experiments were performed to determine the severity and location of the crack extensions in the structure. ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. The combination of a system of acoustic emission sensors, and an ANN could determine crack extension accurately. The difference between predicted and actual crack extensions was determined to be between 0.004 in. and 0.015 in. with 95% confidence. These ANNs, coupled with acoustic emission sensors, showed promise for the creation of an SHM system for aerospace systems.

scholarly journals Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

2015 ◽  
Vol 29 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Izabela Świetlicka ◽  
Siemowit Muszyński ◽  
Agata Marzec
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Radial Basis Function ◽  
Water Activity ◽  
Basis Function ◽  
Acoustic Emission Signal ◽  
Self Organizing Maps ◽  
Emission Signal ◽  
Artificial Neural

Abstract The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

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