scholarly journals Characterization of a Crack by the Acoustic Emission Signal Generated During Propagation

2012 ◽  
Vol 1 ◽  
pp. 266-272 ◽  
Author(s):  
D. Filipussi ◽  
R. Piotrkowski ◽  
J. Ruzzante
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Emission Signal

Characterization of Sheet Metal Forming Using Acoustic Emission

1990 ◽  
Vol 112 (1) ◽  
pp. 44-51 ◽  
Author(s):  
S. Y. Liang ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Acoustic Emission Signal ◽  
Metal Forming ◽  
Energy Content ◽  
Analytical Relationship ◽  
Workpiece Material ◽  
Emission Signal

The application of acoustic emission signal analysis for characterization of sheet metal forming operations is discussed in this paper. Two particular sheet metal forming operations are examined: punch stretching and deep drawing. The acoustic emission signal characteristics, including the energy content, the spectral properties, and time series behaviors, as functions of the process state, are experimentally studied. Using plastic work analysis, an analytical relationship between acoustic emission energy rate and punch stretching parameters (punch feed rate, workpiece thickness, punch size, holder diameter, amount of plastic deformation, and workpiece material properties) is developed and supporting experimental results presented. During the forming processes, acoustic emission signal features show strong correlations with punch/workpiece contact, yielding, deformation, flange wrinkling, necking and fracture. Therefore, acoustic emission can be effectively used for in-process monitoring of sheet metal forming operations.

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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