FilterNet: A deep convolutional neural network for measuring plastic deformation from raw Barkhausen noise waveform

Yan, Zhengxiang; Sun, Guangmin; Liu, Xiucheng; Liu, Yu; He, Cunfu; Xing, Zhixiang; Wang, Xianxian; Zhang, Yangyang; Ning, Mengshuai

doi:10.1016/j.jmmm.2022.169330

Cited by 3 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the macroscopic mechanical properties (surface hardness) of grains under externally applied loads are controlled by microstructures and residual stresses [7,8]. In the micromagnetic detection methods of surface hardness, based on the intrinsic correlation between surface hardness, microstructure, and magnetic properties of a material [9,10], a correlation model between magnetic parameters and surface hardness is established by means of the calibration experimental method [11][12][13]. Before determining this correlation, it is necessary to explore the extraction method of micromagnetic parameters and correlation modeling method [14,15].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

Wang,

Cai,

Liu

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

Wang,

Cai,

Liu

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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

View full text Add to dashboard Cite

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.

show abstract

Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs

Maciusowicz,

Psuj

2024

Applied Sciences

View full text Add to dashboard Cite

The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time–frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training.

show abstract

FilterNet: A deep convolutional neural network for measuring plastic deformation from raw Barkhausen noise waveform

Cited by 3 publications

References 22 publications

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods

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

Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs

Contact Info

Product

Resources

About