Relationship between magnetic nucleation and the microstructure of a hot-deformed permanent magnet: micromagnetic simulation

Tsukahara, Hiroshi; Iwano, Kaoru; Ishikawa, Tadashi; Mitsumata, Chiharu; Ono, Kanta

doi:10.1038/s41427-020-0210-2

Cited by 8 publications

(6 citation statements)

References 58 publications

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“…In figure 2(b) almost the same behaviors are observed as before, except that the magnetization near the compensation point is badly figured because of the appearance of some fluctuations, and this could be due to the weak J int J sh , which equals −0.1 in this case, so the correlation is much weaker between the core and shell spins [34]. One could also assume that spin canting occurs at the interface, where reduced interfacial exchange interaction allows spins closest to the interface to deviate locally from collinear structures [35].…”

Section: J Stat Mech (2023) 033209supporting

confidence: 73%

“…Cubic anisotropy is generally much weaker than uniaxial anisotropy, and has three principal directions which energetically are easy, hard and very hard magnetization directions respectively [28]. It is worth noting that in the case of low anisotropy, each particle constituting the system rotates coherently [28], however, for high anisotropy it is energetically favorable for the system to form domain walls [33,34]. Finally, the last term in equation ( 1) accounts for the interaction of the system spins with the applied external magnetic field B.…”

Section: Computational Detailsmentioning

confidence: 99%

See 1 more Smart Citation

Compensation behavior in (Fe–Ni) core–shell nanostructures: Heisenberg Monte Carlo simulations

Ghazrani¹,

Htoutou²,

Harir³

et al. 2023

J. Stat. Mech.

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By performing atomistic simulations, we have studied some features of classical Heisenberg model using the statistical Monte Carlo method MC under the Hinzke–Nowak algorithm. First, we have deeply explored magnetic and thermal properties of a core–shell nanosphere model and investigate the behaviors of the temperature-dependent magnetization, magnetic susceptibility and phase diagrams for different possible exchange interactions. The obtained results show the existence of diverse -types behaviors in the Néel classification nomenclature. Then, we have applied the same computational method to the real (Fe, Ni) nanostructure using experimental values of magnetic parameters for iron and nickel. It is demonstrated that (Fe, Ni) nanoparticle exhibits a compensation phenomenon compatible with those found in the experimental studies.

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Section: J Stat Mech (2023) 033209supporting

confidence: 73%

Section: Computational Detailsmentioning

confidence: 99%

Compensation behavior in (Fe–Ni) core–shell nanostructures: Heisenberg Monte Carlo simulations

Ghazrani¹,

Htoutou²,

Harir³

et al. 2023

J. Stat. Mech.

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“…The macroscopic properties of permanent magnets arise from the interplay of intrinsic material properties and the magnet's granular microstructure. Traditionally, structural characterization [1], imaging and magnetic measurements [2,3] as well as micromagnetic theory [4] and simulations [5][6][7] have been applied to obtain a better understanding of the impact of microstructure on the magnetic properties. The relation between structure and properties can be treated as mapping between an input space and an ouput space.…”

Section: Introductionmentioning

confidence: 99%

Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning

Kovacs¹,

Exl²,

Kornell³

et al. 2022

Preprint

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We demonstrate the use of model order reduction and neural networks for estimating the hysteresis properties of nanocrystalline permanent magnets from microstructure. With a data-driven approach, we learn the demagnetization curve from data-sets created by grain growth and micromagnetic simulations. We show that the granular structure of a magnet can be encoded within a low-dimensional latent space. Latent codes are constructed using a variational autoencoder. The mapping of structure code to hysteresis properties is a multi-target regression problem. We apply deep neural network and use parameter sharing, in order to predict anchor points along the demagnetization curves from the magnet's structure code. The method is applied to study the magnetic properties of nanocrystalline permanent magnets. We show how new grain structures can be generated by interpolation between two points in the latent space and how the magnetic properties of the resulting magnets can be predicted.

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“…The laborious optimization of Nd-Fe-B magnets toward the desired microstructure and magnetic performance is still in progress [9][10][11][12][13][14][15][16] . To accelerate this process, micromagnetic simulations are often used to elucidate the relationship between the microstructural features and macroscopic magnetic properties 6,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] . Current micromagnetic models of Nd-Fe-B magnets address two cases of high practical importance: sintered magnets 6,8,[20][21][22][23] and hot-deformed ones [24][25][26][27][28][29][30][31][32][33] .…”

mentioning

confidence: 99%

Tomography-based Digital Twin of Nd-Fe-B Permanent Magnets

Bolyachkin,

Dengina,

Kulesh

et al. 2023

Preprint

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Many functional materials are designed at the multiscale level. To properly simulate their physical properties, large and sophisticated computer models capable of replicating the microstructure with nm-level accuracy are required. This is the case for permanent magnets, for which there is a long-standing problem of a significant offset between simulated and experimental coercivity. To overcome this problem and resolve the Brown paradox, we developed a novel approach to construct large-scale finite element models based on the microstructure tomography. It was applied to ultrafine-grained Nd-Fe-B magnets for which, besides the shape, size, and packing of the grains, we reconstructed individual regions of thin intergranular phase separated by triple junctions. Such micromagnetic model managed to reproduce not only experimental coercivity but also its mechanism according to the angular dependence of coercivity. Furthermore, a remarkable role of thin triple junctions as nucleation centers for magnetization reversal was revealed. Proposed digital twins of Nd-Fe-B permanent magnets can assist their optimization towards an ultimate coercivity, while the developed tomography-based approach can be adapted to a wide range of polycrystalline materials.

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Relationship between magnetic nucleation and the microstructure of a hot-deformed permanent magnet: micromagnetic simulation

Cited by 8 publications

References 58 publications

Compensation behavior in (Fe–Ni) core–shell nanostructures: Heisenberg Monte Carlo simulations

Compensation behavior in (Fe–Ni) core–shell nanostructures: Heisenberg Monte Carlo simulations

Exploring the hysteresis properties of nanocrystalline permanent magnets using deep learning

Tomography-based Digital Twin of Nd-Fe-B Permanent Magnets

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