Detection of Acoustic Emission Signal Due to Impact Damage of Composite Materials Based EMD

2019 ◽  
pp. 137-153
Author(s):  
Qingwen You ◽  
Zhefeng Yu ◽  
Jicheng Fang
Keyword(s):  
Acoustic Emission ◽  
Composite Materials ◽  
Acoustic Emission Signal ◽  
Impact Damage ◽  
Emission Signal

Damage characterization in composite materials using acoustic emission signal-based and parameter-based data

2019 ◽  
Vol 178 ◽  
pp. 107469 ◽  
Author(s):  
Claudia Barile ◽  
Caterina Casavola ◽  
Giovanni Pappalettera ◽  
Paramsamy Kannan Vimalathithan
Keyword(s):  
Acoustic Emission ◽  
Composite Materials ◽  
Acoustic Emission Signal ◽  
Emission Signal ◽  
Damage Characterization

scholarly journals ACOUSTIC EMISSION IN THE FRICTION OF COMPOSITE MATERIALS

Aviation ◽  
2014 ◽  
Vol 18 (2) ◽  
pp. 57-63 ◽  
Author(s):  
Sergey Filonenko ◽  
Aleksandr Kosmach
Keyword(s):  
Acoustic Emission ◽  
Composite Material ◽  
Composite Materials ◽  
Acoustic Emission Signal ◽  
Rotation Speed ◽  
Experimental Results ◽  
Material Surface ◽  
Parameter Change ◽  
Surface Layers ◽  
Emission Signal

The model of acoustic emission signal formation during friction and wearing of surfaces of composite materials was examined. The forms of acoustic emission resultant signals were shown. The regularities of parameter change in acoustic emission resultant signals with an increasing rotation speed were determined according to the results of modeling. It was found that theoretical and experimental results of acoustic emission signals registered during friction of composite material surface layers agree well.

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]

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