Enhancing the precision of magnetocrystalline anisotropy energy estimation from Barkhausen Noise using a deep neural network-Reference-Cited by-同舟云学术

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Enhancing the precision of magnetocrystalline anisotropy energy estimation from Barkhausen Noise using a deep neural network

Published:2020-09 Issue: Volume:24 Page:101145
ISSN:2352-4928
Container-title:Materials Today Communications
language:en
Short-container-title:Materials Today Communications

Author:

Perez-Montes Filiberto,Ortega-Labra Omar,Manh Tu Le,Perez-Benitez J.A.

Funder

CONACYT, Mexico, and I

nstituto Politecnico Nacional

Publisher

Elsevier BV

Subject

Materials Chemistry,Mechanics of Materials,General Materials Science

Reference26 articles.

1. Introduction to Magnetic materials;Cullity,1983

2. Electronic Structure and Magnetism of Complex Materials;Halilov,2003

3. Texture Analysis in Material Science London;Bunge,1982

4. Phenomenology and interpretation of the barkhausen effect in ferromagnetic materials;Alessandro;J. Appl. Phys.,1988

5. Dynamics of domain magnetization and the Barkhausen effect Czechoslovak;Jiles;J. Phys.,2000

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1. Surface Integrity Evaluation Based on Barkhausen Noise Analysis: A Conceptual Approach;Procedia CIRP;2024

2. Feature extraction and evaluation for quantitative prediction of hardness in bearing steel based on magnetic Barkhausen noise;NDT & E International;2023-10

3. Evaluation of Pipeline Steel Mechanical Property Distribution Based on Multimicromagnetic NDT Method;IEEE Transactions on Instrumentation and Measurement;2023

4. Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms;Applied Sciences;2022-12-06

5. 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;2021-12-24

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