Field assisted sintering and spark plasma texturing of Nd–Fe–B magnets with anisotropic magnetic properties
Bibliographic record
Abstract
Field assisted sintering technologies like spark plasma sintering (FAST/SPS) are attractive alternative methods for the processing of nanocrystalline Nd–Fe–B magnets with well-pronounced anisotropic magnetic properties. This work aims to investigate the potential of hot deformation through FAST/SPS, a method commonly referred to as spark plasma texturing (SPT). SPT with its fine-tuned and closely monitored parameters of heating rate and applied uniaxial pressure has the possibility to yield further refined microstructure and reproducibility when compared to traditional hot deformation. This fine control has the potential to expand beyond the consolidation of nanocrystalline melt-spun starting powder and into other starting materials. Here, the focus is on two different routes of SPT both starting from the same commercial melt-spun Nd–Fe–B powder (Magnequench MQU-F). One deals with the SPT of semi-dense MQU-F compacts (∼70% density), while the other focuses on deformation of MQU-F fully dense compacts. Semi-dense compact SPT could lead to new routes for the consolidation of anisotropic Nd–Fe–B magnet scrap without inducing excessive grain growth, while dense compact SPT has more potential for highly textured microstructure. The best balance of properties for a magnet produced from a semi-dense compact was B r = 1.18 T, H cJ = 1203 kA m −1 and (BH) max = 249 kJ m −3 , which was achieved by deforming a semi-dense compact at 800 °C applying a pressure of 100 MPa. When starting from a dense compact, the best performance was B r = 1.38 T, H cJ = 1180 kA m −1 and (BH) max = 353 kJ m −3 . Here, deformation was performed at 750 °C under 70 MPa of pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".