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Author: Woodcock, Thomas George ×

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    Rare-earth-free MnAl-C-Ni permanent magnets produced by extrusion of powder milled from bulk
    (Amsterdam : Elsevier, 2020) Feng, Le; Freudenberger, Jens; Mix, Torsten; Nielsch, Kornelius; Woodcock, Thomas George
    Rare-earth-free MnAl-C-Ni permanent magnets have been produced for the first time by extruding powders milled from bulk. The resulting materials, fabricated using different conditions, contained a large volume fraction (> 0.92) of the desired τ-phase. In terms of the maximum energy product, the best performance obtained for a whole, transverse section of the extruded material was (BH)max = 46 kJm−3, and was (BH)max = 49 kJm−3 for a sample taken from near the edge of this section. Analysis showed that this material was comparable to the long-established benchmark, comprising MnAl-C-based magnets extruded in industry from bulk or from gas-atomised powder. Such materials are no longer available. The microstructure of the materials produced here consisted of fine, recrystallised grains, which exhibited an <001> fibre texture with intermediate texture quality and of larger, non-recrystallised regions, which contained hierarchical twinning and a high density of defects. The volume fraction and size of the non-recrystallised regions was greatly reduced by decreasing the size of the initial powder particles. This led to a large increase in the squareness factor of the demagnetisation curve and consequently to the high (BH)max values observed.
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    Automated meshing of electron backscatter diffraction data and application to finite element micromagnetics
    (Amsterdam [u.a.] : Elsevier, 2019) Gusenbauer, Markus; Fischbacher, Johann; Kovacs, Alexander; Oezelt, Harald; Bance, Simon; Zhao, Panpan; Woodcock, Thomas George; Schrefl, Thomas
    This paper gives a procedure for automatically generating finite element meshes with an adaptive mesh size from Electron Backscatter Diffraction (EBSD) data. After describing the procedure in detail, including preliminary and image processing steps, an example application is given. The method was used to carry out finite element (FE) micromagnetic simulations based on real microstructures in the hard magnetic material, MnAl. A fast micromagnetic solver was used to compute hysteresis properties from the finite element mesh generated automatically from EBSD data. The visualization of the magnetization evolution showed that the reversal is governed by domain wall pinning at twin boundaries. The calculated coercive fields are very sensitive to changes of the Gilbert damping constant, even for low field rates. © 2019 The Authors
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    Extracting local nucleation fields in permanent magnets using machine learning
    (Berlin : Springer Nature, 2020) Gusenbauer, Markus; Oezelt, Harald; Fischbacher, Johann; Kovacs, Alexander; Zhao, Panpan; Woodcock, Thomas George; Schrefl, Thomas
    Microstructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict the macroscopic coercivity from microstructure imaging. Although this is not possible now, in the present work we examine a related question, namely the prediction of simulated nucleation fields of a quasi-three-dimensional (rescaled and extruded) system constructed from a two-dimensional image. We extract features of the image and analyze them via machine learning. A large number of extruded systems are constructed from 10 × 10 pixel sub-images of an Electron Backscatter Diffraction (EBSD) image using an automated meshing procedure. A local nucleation field is calculated by micromagnetic simulation of each quasi-three-dimensional system. Decision trees, trained with the simulation results, can predict nucleation fields of these quasi-three-dimensional systems from new images within seconds. As for now we cannot quantitatively predict the macroscopic coercivity, nevertheless we can identify weak spots in the magnet and see trends in the nucleation field distribution.