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Record W4408429621 · doi:10.2497/jjspm.14e-t16-02

Field Assisted Sintering in the Direct Recycling of Hot Deformed Nd-Fe-B Magnet Scrap

2025· article· en· W4408429621 on OpenAlex
Monica Keszler, Felix Großwendt, Anna-Caroline Assmann, Martin Krengel, Fernando Maccari, Oliver Gutfleisch, Doris Sebold, Sebastian Weber, Olivier Guillon, Martin Bram

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Japan Society of Powder and Powder Metallurgy · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties of Alloys
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMaterials scienceScrapSinteringMagnetMetallurgyField (mathematics)Mechanical engineeringEngineering

Abstract

fetched live from OpenAlex

With the expanding use of high-performance permanent magnets, such as Nd-Fe-B, in green energy production and e-mobility, comes the need to further investigate contemporary production methods. Field assisted sintering technologies, such as spark plasma sintering and flash spark plasma sintering, have shown promising results with regards to anisotropic texturing, densification, and microstructural fine-tuning, even from non-ideal crushed anisotropic Nd-Fe-B scrap as a starting powder. Optimization of these processes could lead to magnetic performance that matches or exceeds standard commercial magnet production techniques, such as hot deformation. This is due to the fine parameter monitoring and control available with field assisted sintering devices. This study focuses on the optimization of field assisted sintering with commercial MQU-F to demonstrate net-shaping of anisotropic Nd-Fe-B magnets with an intention of transferring the parameters to the deformation of recycled magnet powder.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.254
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it