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Sensors 2017, 17(4), 789; doi:10.3390/s17040789

The Identification of the Deformation Stage of a Metal Specimen Based on Acoustic Emission Data Analysis

1
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
2
Beijing Key Lab of Membrane Science and Technology, College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 24 February 2017 / Revised: 24 March 2017 / Accepted: 26 March 2017 / Published: 7 April 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [8719 KB, uploaded 7 April 2017]   |  

Abstract

The acoustic emission (AE) signals of metal materials have been widely used to identify the deformation stage of a pressure vessel. In this work, Q235 steel samples with different propagation distances and geometrical structures are stretched to get the corresponding acoustic emission signals. Then the obtained acoustic emission signals are de-noised by empirical mode decomposition (EMD), and then decomposed into two different frequency ranges, i.e., one mainly corresponding to metal deformation and the other mainly corresponding to friction signals. The ratio of signal energy between two frequency ranges is defined as a new acoustic emission characteristic parameter. Differences can be observed at different deformation stages in both magnitude and data distribution range. Compared with other acoustic emission parameters, the proposed parameter is valid in different setups of the propagation medium and the coupled stiffness. View Full-Text
Keywords: acoustic emission; metal deformation degree; tensile test; signal energy ratio; empirical mode decomposition acoustic emission; metal deformation degree; tensile test; signal energy ratio; empirical mode decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zou, S.; Yan, F.; Yang, G.; Sun, W. The Identification of the Deformation Stage of a Metal Specimen Based on Acoustic Emission Data Analysis. Sensors 2017, 17, 789.

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