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

Perez-Montes, Filiberto; Ortega-Labra, Omar; Manh, Tu Le; Benítez, José Alberto Pérez

doi:10.1016/j.mtcomm.2020.101145

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Various methods, such as machine learning algorithms, shallow and deep artificial neural networks, are used. In the context of MBN, several works [26][27][28][29] using artificial intelligence methods to extract information from signals can be found in the literature. In [26], a Deep Convolutional Neural Network (DCNN) was used to classify various CGO and HGO electrical sheets subjected to a different method of surface engineering.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the proposed deep network model allowed for the evaluation of the angular deviation of a magnetic easy axis from the rolling direction. In [27], the MBN signals expressed in the time domain and autoencoders were used to estimate the magnetocrystalline energy of the examined steel, whereas in [28] a convolutional neural network for the quantitative description of plastic deformation was proposed. Last but not least, paper [29] presented an exploratory data analysis and the application of machine learning methods to describe the surface quality of a material.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Maciusowicz

Psuj

2022

Applied Sciences

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In this paper, a combination of Magnetic Barkhausen Noise (MBN) and several classical machine learning (ML) methods were used to evaluate both the grade and the magnetic directions of conventional and high grain oriented electrical sheets subjected to selected surface engineering methods. The presented analysis was conducted to compare the performance of two machine learning approaches, classical ML and deep learning (DL), in reference to the same MBN examination problem and based on the same database. Thus, during the experiment, 26 classical ML algorithms were used including decision trees, discriminant analysis, support vector machines, naïve Bayes, nearest neighbor, artificial neural networks and ensemble classifiers. The experiments were carried out considering a different number of recognized magnetic directions and hence the number of determined classes as well. The results of classification accuracy of the applied ML methods were compared with those obtained for the DL model presented in a previous paper. The highest accuracy was obtained for ML models based on artificial neural networks and ensemble bagged trees. However, the accuracy did not reach 89% in the best case—for the smallest number of determined classes. Nevertheless, the achieved results generally indicated an approx. 10 percent advantage of the deep learning model over the classical ones in terms of accuracy in each of the considered cases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Maciusowicz

Psuj

2022

Applied Sciences

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“…As well, they meet the rapidly growing interest in their application in non-destructive testing, for example, to control the condition of building structures [ 42 , 43 ], railway structures [ 44 ], or testing the properties of materials [ 45 ]. Deep networks based on autoencoders have also been successfully used to analyse the information contained in the time series of the Barkhausen noise signal for automatic and precise estimation of magnetocrystalline energy [ 46 ]. Due to the possibilities of Deep Neural Networks (DNN) and their sensitivity to even subtle changes in the input data, it is possible to apply them to the analysis of the MBN signal obtained from electrical steel sheets.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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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Surface Integrity Evaluation Based on Barkhausen Noise Analysis: A Conceptual Approach

Lötfering,

Schrank,

Rüppel

et al. 2024

Procedia CIRP

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

Cited by 5 publications

References 14 publications

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

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

Surface Integrity Evaluation Based on Barkhausen Noise Analysis: A Conceptual Approach

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