Characterization of angular dependence of macroscopic magnetic properties in ASTM 36 steel using magnetic Barkhausen noise

2007 ◽  
Vol 40 (4) ◽  
pp. 284-288 ◽  
Author(s):  
J.A. Pérez-Benitez ◽  
J. Capó-Sánchez ◽  
L.R. Padovese
Keyword(s):  
Magnetic Properties ◽  
Angular Dependence ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise

Microstructural Magnetic Characterization of Annealed Non-Oriented Electrical Steel

2018 ◽  
Vol 915 ◽  
pp. 190-195
Author(s):  
Evangelos Hristoforou ◽  
Athanasios G. Mamalis
Keyword(s):  
Magnetic Properties ◽  
Magnetic Permeability ◽  
Electrical Steel ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Magnetic Characterization ◽  
Microstructural Changes ◽  
Wide Range ◽  
Nondestructive Characterization

The present paper investigates the utilization of the magnetic Barkhausen noise and magnetic permeability methods for the nondestructive characterization of annealed non-oriented electrical steel samples which were isothermally annealed in a wide range of temperatures (400°C – 950°C) and subsequently cooled in air. The resulting magnetic properties were compared with the microstructural changes occurring during annealing.

Characterization of Electron Beam Welded Non-Oriented Electrical Steel with Magnetic Barkhausen Noise

2014 ◽  
Vol 605 ◽  
pp. 39-42 ◽  
Author(s):  
Polykseni Vourna
Keyword(s):  
Mechanical Properties ◽  
Magnetic Properties ◽  
Electron Beam ◽  
Electrical Steel ◽  
Electron Beam Welding ◽  
Magnetic Measurements ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Electrical Steels

In this paper the influence of Electron Beam Welding (EBW) on the microstructure, mechanical and magnetic properties of Non-Oriented Electrical steels was presented and evaluated. Single pass welds free of defects were produced at welding speeds and pulsed currents following a predesigned protocol. The samples microstructure and the macrohardness tests were concluded with the magnetic measurements (Barkhausen Noise) in order to correlate the structural and mechanical properties with the magnetizing behavior of Non-Oriented Electrical Steel.

Characterization of microstructures in 17-4-PH stainless steel by magnetic Barkhausen noise analysis

1993 ◽  
Vol 26 (3) ◽  
pp. 141-148 ◽  
Author(s):  
D.K. Bhattacharya ◽  
T. Jayakumar ◽  
V. Moorthy ◽  
S. Vaidyanathan ◽  
Baldev Raj
Keyword(s):  
Stainless Steel ◽  
Noise Analysis ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise

Magnetic Barkhausen Noise for Characterization of Recovery and Recrystallization

2010 ◽  
Vol 46 (2) ◽  
pp. 513-516 ◽  
Author(s):  
Kizkitza Gurruchaga ◽  
Ane Martinez-de-Guerenu ◽  
Miguel Soto ◽  
Fernando Arizti
Keyword(s):  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise

Characterization of the magnetic easy axis in pipeline steel using magnetic Barkhausen noise

1994 ◽  
Vol 75 (12) ◽  
pp. 7983-7988 ◽  
Author(s):  
Thomas W. Krause ◽  
L. Clapham ◽  
David L. Atherton
Keyword(s):  
Easy Axis ◽  
Pipeline Steel ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Magnetic Easy Axis

Influence of the maximum applied magnetic field on the angular dependence of Magnetic Barkhausen Noise in API5L steels

2016 ◽  
Vol 401 ◽  
pp. 108-115 ◽  
Author(s):  
P. Martínez-Ortiz ◽  
J.A. Pérez-Benítez ◽  
J.H. Espina-Hernández ◽  
F. Caleyo ◽  
N. Mehboob ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Angular Dependence ◽  
Applied Magnetic Field ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise

scholarly journals Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

Materials ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 118
Author(s):  
Michal Maciusowicz ◽  
Grzegorz Psuj ◽  
Paweł Kochmański
Keyword(s):  
Fourier Transform ◽  
Barkhausen Noise ◽  
Magnetic Barkhausen Noise ◽  
Short Time Fourier Transform ◽  
Deep Convolutional Neural Networks ◽  
Electrical Steels ◽  
New Approach ◽  
Time Frequency ◽  
Short Time

This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time–frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials.

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