scholarly journals Selective Magnetic Abrasive Finishing of Nano-Thickness IZO-Coated Pyrex Glass Using Acoustic Emission Monitoring and Artificial Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 136783-136791
Author(s):  
Jungsun Kim ◽  
Hyojeong Kim ◽  
Seoung Hwan Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Pyrex Glass ◽  
Acoustic Emission Monitoring ◽  
Magnetic Abrasive Finishing ◽  
Emission Monitoring ◽  
Abrasive Finishing ◽  
Artificial Neural

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

scholarly journals Effect of Freeze–Thaw Cycling on the Failure of Fibre-Cement Boards, Assessed Using Acoustic Emission Method and Artificial Neural Network

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2181 ◽  
Author(s):  
Tomasz Gorzelańczyk ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Method ◽  
Emission Method ◽  
Three Point Bending ◽  
Freeze Thaw ◽  
Fibre Cement ◽  
Artificial Neural ◽  
Building Practice

This paper presents the results of investigations into the effect of freeze–thaw cycling on the failure of fibre-cement boards and on the changes taking place in their structure. Fibre-cement board specimens were subjected to one and ten freeze–thaw cycles and then investigated under three-point bending by means of the acoustic emission method. An artificial neural network was employed to analyse the results yielded by the acoustic emission method. The investigations conclusively proved that freeze–thaw cycling had an effect on the failure of fibre-cement boards, as indicated mainly by the fall in the number of acoustic emission (AE) events recognized as accompanying the breaking of fibres during the three-point bending of the specimens. SEM examinations were carried out to gain better insight into the changes taking place in the structure of the tested boards. Interesting results with significance for building practice were obtained.

scholarly journals Acoustic Emission Signal Recognition of Different Rocks Using Wavelet Transform and Artificial Neural Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Xiangxin Liu ◽  
Zhengzhao Liang ◽  
Yanbo Zhang ◽  
Xianzhen Wu ◽  
Zhiyi Liao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Good Method ◽  
Rock Types ◽  
Fracturing Process ◽  
Different Types ◽  
Artificial Neural ◽  
Ae Signals

Different types of rocks generate acoustic emission (AE) signals with various frequencies and amplitudes. How to determine rock types by their AE characteristics in field monitoring is also useful to understand their mechanical behaviors. Different types of rock specimens (granulite, granite, limestone, and siltstone) were subjected to uniaxial compression until failure, and their AE signals were recorded during their fracturing process. The wavelet transform was used to decompose the AE signals, and the artificial neural network (ANN) was established to recognize the rock types and noise (artificial knock noise and electrical noise). The results show that different rocks had different rupture features and AE characteristics. The wavelet transform provided a powerful method to acquire the basic characteristics of the rock AE and the environmental noises, such as the energy spectrum and the peak frequency, and the ANN was proved to be a good method to recognize AE signals from different types of rocks and the environmental noises.

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