Model for the correlation between magnetocrystalline energy and Barkhausen noise in ferromagnetic materials

Manh, Tu Le; Caleyo, F.; Hallen, J.M.; Espina-Hernández, J.H.; Benítez, José Alberto Pérez

doi:10.1016/j.jmmm.2018.01.066

Cited by 24 publications

(13 citation statements)

References 28 publications

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“…In general, these steels have a ferritic/pearlitic microstructure with different grain size. The equiaxial grain morphology was observed on the rolling plane with a relatively homogeneous distribution, while elongated grains were observed on the transverse (RD–ND and TD–ND) planes due to the pipe manufacturing process [ 31 , 32 , 33 ]. Microstructural parameters such as grain size and volume fraction of the ferrite phase were determined by optical microscopy, as shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

“…Figure 4 shows the cubic–orthorhombic representation of the X-ray-derived ODF of the studied steels in the ϕ 2 = 45 section of Euler space before the electrochemical tests [ 30 ]. It can be seen that the crystallographic texture of these steels was characterized by the presence of the {100}||ND, {111}||ND, {112}||ND, and {110}||ND texture fibers with different degrees of development [ 24 , 32 , 33 ]. The volume fractions ( V { hkl } ND) of these fibers were calculated as shown in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

“…The volume fractions ( V { hkl } ND) of these fibers were calculated as shown in Table 3 . The formation of these texture fibers can be explained as the consequence of the hot rolling process during manufacture of the pipe [ 31 , 32 , 33 ]. X52 and X60 steels ( Figure 4 b,c) showed a texture close to random, with a relatively small degree of difference, while the X56 steel ( Figure 4 a) presented a markedly strong texture dominated by {112}||ND and {111}||ND fibers.…”

Section: Resultsmentioning

confidence: 99%

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Model for the Correlation between Anodic Dissolution Resistance and Crystallographic Texture in Pipeline Steels

et al. 2018

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This paper presents a novel physical–mathematical model to describe the relationship between the crystallographic texture and corrosion behavior of American petroleum institute (API) 5L steels. Symmetric spherical harmonic functions were used to estimate the material’s corrosion resistance from crystallographic texture measurements. The predictions of the average corrosion resistance index made from the crystallographic texture were in good agreement with those obtained from potentiodynamic polarization and electrochemical impedance spectroscopy measurements for the studied steels. This agreement validates the capacity of this model and opens the possibility of applying it as a novel criterion for materials selection and design stages to combat corrosion problems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Model for the Correlation between Anodic Dissolution Resistance and Crystallographic Texture in Pipeline Steels

et al. 2018

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“…The Barkhausen effect specifically refers to the phenomenon in which the magnetic flux of a magnetized material changes discontinuously due to magnetic domain inversion during magnetization [ 1 ]. As an important nondestructive testing (NDT) method, the magnetic Barkhausen noise (MBN) signal is sensitive to the changes in many material properties and has many applications in the fields of material stress and hardness detection [ 2 , 3 , 4 , 5 ], metal fatigue state analysis [ 6 ], metal microstructure transformation, and grain size measurement [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Hou

Zheng

et al. 2020

Sensors

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The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback–Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

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“…MBN could constitute an elegant alternative solution to evaluate MAE. The sensitivity of the MBN raw signal to MAE has already been demonstrated especially in the saturation remanence branch[70]. In this study, PMNP and MBNenergy(H) hysteresis cycles have been combined to establish the MAE sensitivity of four indicators, directly read on the MBNenergy(H) hysteresis cycles:…”

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confidence: 99%

Directional magnetic Barkhausen noise measurement using the magnetic needle probe method

Ducharne

Deffo

Tsafack

et al. 2021

Journal of Magnetism and Magnetic Materials

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A directional local magnetic measurement technique as example of measuring the angular dependence of the magnetic Barkhausen noise in electrical and construction steels at typical quasi-static frequency by the magnetic needle probe (MNP) method is presented. The directional measurement configuration of the MNP method is typical of a half-turn search coil sensor coupled to high gain analogue amplification and high order filtration stages, owing to its very weak induced electromotive force. The method exploits the MBNenergy(H) hysteresis cycle (time integration of the square of the MBN voltage signal as a function of the tangent excitation field H) and brings forth stable indicators possibly related to the Magnetocrystalline Anisotropy Energy (MAE). Experimental tests performed on grain-oriented Fe-Si and low carbon steel specimens, and comparison with the conventional search coil measurement results were worth the validation of the MNP method. These results put to the front the good directional selectivity of the MNP method over the conventional MBN sensors adversely affected by the transverse fields at the material surface. Eventually, the printed magnetic needle probe (PMNP) sensor was relatedly described to provide non-invasive directional measurements for continuous structural health monitoring.

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Model for the correlation between magnetocrystalline energy and Barkhausen noise in ferromagnetic materials

Cited by 24 publications

References 28 publications

Model for the Correlation between Anodic Dissolution Resistance and Crystallographic Texture in Pipeline Steels

Model for the Correlation between Anodic Dissolution Resistance and Crystallographic Texture in Pipeline Steels

A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis

Directional magnetic Barkhausen noise measurement using the magnetic needle probe method

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