Detection of Magnetomechanical Effect in Structural Steel Using GMR 2nd Order Gradiometer Based Sensors

Bonavolontà, C.; Valentino, M.; Penta, Francesco; Granata, C.; Ruggiero, B.; Silvestrini, P.; Vettoliere, A.

doi:10.3390/s19194147

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…Recent works [12][13][14][15] have proposed the use of magnetoresistive sensors, due to its high sensitivity to magnetic fields, for estimating the stress using the ME. In particular, Werner Riken et al [12] used a Giant Magnetoresistance (GMR) and eddy current sensor for estimating the stress in steel wires.…”

Section: Introductionmentioning

confidence: 99%

Magnetic field patterns as a tool for stress estimation in steel samples

Martínez-Ortiz,

Le Manh,

Pérez-Benítez

2023

Meas. Sci. Technol.

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In this work, steel square samples were subjected to varying degrees of compression, resulting in a distribution of simultaneous compressive and tensile stresses within the samples. The tangential component of the surface magnetic flux density was mapped along both the direction of compression and perpendicular to it, using a magnetic sensor, for each level of compression. The results show that the maps of the relative difference of magnetic flux density for different levels of applied stress, with respect to the flux density at zero stress, exhibited a diagonal stripes pattern. This pattern was discovered thanks to the particular surface stress distribution in square samples. The formation of these patterns could be attributed to the re-orientation of magnetic domains due to stress and the magneto-elastic effect, which was confirmed by the change in orientation of the magnetic flux density vectors obtained from the measurement of two of their components. Additionally, the maps showed an increase in contrast between high and low values with an increase in applied stress. Finally, the maps of the module of the gradient of magnetic flux density displayed a distinct diagonal chessboard-like pattern, and the contrast between low and high values increased with an increase in applied stress.

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Section: Introductionmentioning

confidence: 99%

Magnetic field patterns as a tool for stress estimation in steel samples

Martínez-Ortiz,

Le Manh,

Pérez-Benítez

2023

Meas. Sci. Technol.

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“…Spin-tunnel magnetoresistive nanostructures are used in various spintronic devices: in magnetic field sensors [ 1 , 2 , 3 , 4 ], in magnetoresistive biosensors [ 5 ], and in magnetoresistive memory elements [ 6 , 7 , 8 ]. The magnetic tunnel junction (MTJ) consists of a conducting free magnetic layer (FL), a dielectric tunnel barrier, and a conducting fixed magnetic layer (FixL) [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The density of the magnetostatic energy of the interaction of the stripes is considered to be

where

is the magnetization vector of the second stripe; and

is the magnetic field created by the first stripe in the area of the second stripe, which was considered uniform and coinciding with the demagnetizing field inside the ellipsoid—that is, inside the first stripe. The same author in [ 4 ] developed the theory for the case of stripes with different thicknesses (ellipsoids). In [ 5 ], an attempt was made to take into account the difference between the magnetic field of the FixL outside the stripe and the demagnetization field inside this stripe, assuming that this field is proportional to the demagnetization field.…”

Section: Introductionmentioning

confidence: 99%

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Development and Research of a Theoretical Model of the Magnetic Tunnel Junction

Polyakov

Amelichev

Zhukov

et al. 2021

Sensors

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Spin-dependent tunneling structures are widely used in many spintronic devices and sensors. This paper describes the magnetic tunnel junction (MTJ) characteristics caused by the inhomogeneous magnetic field of ferromagnetic layers. The extremely oblate magnetic ellipsoids have been used to mimic these layers. The strong effect of an inhomogeneous magnetic field on the magnetoresistive layers’ interaction was demonstrated. The magnetostatic coupling coefficient is also calculated.

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“…[11,12] Flexible magnetic films have great potential for wireless multimodal flexible sensor due to the curvature and azimuth angle dependent magnetic anisotropy. [13][14][15][16] Former studies have introduced the sensing functions like correcting localization of the plastic deformation on the sample surface. [17,18] To realize the bending sensing function, ferromagnetic resonance has been reported as an effective sensing technology due to the strong correlation between mechanical bending and magnetic anisotropy.…”

mentioning

confidence: 99%

Domain‐Engineered Flexible Ferrite Membrane for Novel Machine Learning Based Multimodal Flexible Sensing

Shen

Liu

et al. 2022

Adv Materials Inter

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To run into different usage scenarios, tremendous kinds of bending sensors based on resistive, capacitive, piezoelectric responses, or optical theories have been prepared, which encompass a broad scope of novel materials, such as carbon nanotubes, metallic nanowires, carbonized silk fabrics, and fiber-optics, etc. [7][8][9][10] Despite those impressive performances, the development of novel flexible materials that possess multimodal sensing capabilities for the simultaneous detection of bending curvature and position information remains challenging, which is very critical in novel adhesion-free flexible sensing systems. [11,12] Flexible magnetic films have great potential for wireless multimodal flexible sensor due to the curvature and azimuth angle dependent magnetic anisotropy. [13][14][15][16] Former studies have introduced the sensing functions like correcting localization of the plastic deformation on the sample surface. [17,18] To realize the bending sensing function, ferromagnetic resonance has been reported as an effective sensing technology due to the strong correlation between mechanical bending and magnetic anisotropy. [19] In addition, the microwave absorption performance endows it with great potential in wireless sensing. [20] For instance, Figure 1a shows the 1D sensing relationship between bending curvature radius R and ferromagnetic resonance field (H r ) for flexible high-quality epitaxial Flexible materials and devices that can simultaneously reflect multimodal information are highly desired for novel flexible electronics and intelligent flexible sensing systems. In this regard, flexible magnetic films have great potential for wireless multimodal flexible sensor due to the curvature and azimuth angle-dependent ferromagnetic resonance. However, a key challenge now is to build the precise relationship among the mechanical bending, azimuth angle, and the ferromagnetic resonance of the film, which involves multi-physics and coupled process. In this work, the physical problem is solved by combining material engineering and machine learning. Material domain engineering is applied to form localized multi-peak ferromagnetic resonance features for increasing sensitivity. Besides, convolutional neural network algorithm is utilized to help recognize the bending and azimuth angle modulated ferromagnetic resonance in flexible film systems. It is found that the bending information for the flexible film with engineered domain structure can be mapped to the ferromagnetic profile with accuracy over 99%, while the accuracy sharply decreases to less than 50% in the control group of high-quality film. This study provides a versatile platform for developing machine learning-based novel sensing materials.

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Detection of Magnetomechanical Effect in Structural Steel Using GMR 2nd Order Gradiometer Based Sensors

Cited by 6 publications

References 18 publications

Magnetic field patterns as a tool for stress estimation in steel samples

Magnetic field patterns as a tool for stress estimation in steel samples

Development and Research of a Theoretical Model of the Magnetic Tunnel Junction

Domain‐Engineered Flexible Ferrite Membrane for Novel Machine Learning Based Multimodal Flexible Sensing

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