On the Formation and Evolution of Cu–Ni-Rich Bridges of Alnico Alloys With Thermomagnetic Treatment

Min, Fan; Liu, Y.; Jha, Rajesh; Dulikravich, George S.; Schwartz, J.; Koch, Carl C.

doi:10.1109/tmag.2016.2555956

Cited by 41 publications

(25 citation statements)

References 20 publications

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“…These results show that the Ni-rich α 3 phase in real alnico samples has a general TM to Al ratio of 3:1 and is small in size, which is consistent with observation of a Ni-enriched rod-shaped phase in commercial alnico 9 [31]. Cu-rich and Ni-rich bridges were also observed in lab alnico samples with composition close to those of commercial alnico 8 and 9 [32] and our results should also be noted for commercial alnico 8 showing Ni-rich clusters/phases (see Supplemental Material [20]).…”

Section: Dft)supporting

confidence: 89%

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Cluster-Expansion Model for Complex Quinary Alloys: Application to Alnico Permanent Magnets

et al. 2017

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An accurate and transferable cluster expansion model for complex quinary alloys was developed. Lattice Monte Carlo simulation enabled by this cluster expansion model was used to investigate temperature dependent atomic structure of alnico alloys, which have been considered as promising high performance non-rare earth permanent magnet materials for high temperature applications. The results from Monte Carlo simulations are consistent with available experiment data and provide useful insights into phase decomposition, selection and chemical ordering in alnico. The simulations also reveal a previously unrecognized D0 3 alloy phase. This phase is very rich in Ni and exhibits very weak magnetization. Manipulating the size and location of this phase provides a possible route to improve the magnetic properties of alnico, especially coercivity.

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Section: Dft)supporting

confidence: 89%

“…Hence, the α 1 magnetic needles will be magnetically well decoupled from each other, then the coercivity of alnico will be enhanced. Recent experimental work on alnico showed the signal of this "skin layer" [32].…”

Section: Dft)mentioning

confidence: 99%

Cluster-Expansion Model for Complex Quinary Alloys: Application to Alnico Permanent Magnets

et al. 2017

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“…These materials have attracted renewed attention in the context of magnetic materials that are free of rare-earth elements and do not contain other expensive elements, such as Pt [7][8][9][10][11][12][13][14]. The magnetic anisotropy of alnicos reflects their peculiar nanostructure, where highmagnetization rods with an approximate composition of FeCo (α 1 -phase) are embedded in an essentially nonmagnetic Al-Ni-rich matrix (α 2 -phase) [4][5][6][7][14][15][16][17][18].…”

mentioning

confidence: 99%

Simulation of alnico coercivity

Skomski

Hoffmann

et al. 2017

Applied Physics Letters

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Micromagnetic simulations of alnico show substantial deviations from Stoner-Wohlfarth behavior due to the unique size and spatial distribution of the rod-like Fe-Co phase formed during spinodal decomposition in an external magnetic field. The maximum coercivity is limited by single-rod effects, especially deviations from ellipsoidal shape, and by interactions between the rods. Both the exchange interaction between connected rods and magnetostatic interaction between rods are considered, and the results of our calculations show good agreement with recent experiments. Unlike systems dominated by magnetocrystalline anisotropy, coercivity in alnico is highly dependent on size, shape, and geometric distribution of the Fe-Co phase, all factors that can be tuned with appropriate chemistry and thermal-magnetic annealing.

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“…Machine learning approaches have been previous used to help reduce the time required in the alloy design process. [28][29][30][31][32][33][34][35][36][37][38] Supervised machine learning approaches such as artificial neural networks, [37][38][39][40] k-Nearest Neighbour algorithm (k-NN), 38,41 genetic programming, 37,38,40,42 kriging, 43,44 and unsupervised approaches such as Principal Component Analysis (PCA), 30,31,35 Hierarchical Clustering Analysis (HCA), 29,31,34 and Self Organizing Maps (SOM) 28 have been previously used in materials science and can also be helpful in this case. From an implementation point of view, there exist several open-source software packages to develop response surfaces or metamodels using several different concepts from artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys

Jha

Chakraborti

Diercks

et al. 2018

Computational Materials Science

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FINEMET alloys have desirable soft magnetic properties due to the presence of Fe 3 Si nanocrystals with specific size and volume fraction. To guide future design of these alloys, we investigate relationships between select processing parameters (composition, temperature, annealing time) and structural parameters (mean radius and volume fraction) of the Fe 3 Si domains. We present a combined CALPHAD and machine learning approach leading to well-calibrated metamodels able to predict structural parameters quickly and accurately for any desired inputs. To generate data, we have used a known precipitation model to perform annealing simulations at a several temperatures, for varying Fe and Si concentrations. Thereafter, we used the data to develop metamodels for mean radius and volume fraction via the k-Nearest Neighbour algorithm. The metamodels reproduce closely the results from the precipitation model over the entire annealing timescale. Our analysis via parallel coordinate charts shows the effect of composition, temperature, and annealing time, and helps identify combinations thereof that lead to the desired mean radius and volume fraction for nanocrystals. This work contributes to understanding the linkages between processing parameters and desired microstructural characteristics responsible for achieving targeted properties, and illustrates ways to reduce the time from alloy discovery to deployment.

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On the Formation and Evolution of Cu–Ni-Rich Bridges of Alnico Alloys With Thermomagnetic Treatment

Cited by 41 publications

References 20 publications

Cluster-Expansion Model for Complex Quinary Alloys: Application to Alnico Permanent Magnets

Cluster-Expansion Model for Complex Quinary Alloys: Application to Alnico Permanent Magnets

Simulation of alnico coercivity

Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys

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