Influence of process parameters on the density and magnetic properties of laser powder bed fusion NdFeB magnets
Bibliographic record
Abstract
The demand for high-performance NdFeB permanent magnets is rapidly increasing due to their critical role in electric vehicles, wind turbines, robotics, and advanced electronics. Traditional manufacturing methods, such as sintering and bonding, are limited in design complexity, material efficiency, and sustainability. These methods require extensive machining, generate substantial waste, and often involve hazardous processing steps. Additionally, global supply chain concerns surrounding rare earth elements have intensified the need for more sustainable, resource-efficient, and locally adaptable manufacturing approaches. Laser Powder Bed Fusion (PBF-LB) presents a promising alternative to traditional methods, enabling the production of intricate geometries optimized for magnetic performance without extensive post-processing or material loss. Moreover, PBF-LB facilitates precise microstructure control to tailor magnetic properties for specific application requirements. This study examines the influence of PBF-LB process parameters on the density and magnetic properties of 3D-printed Nd₇.₅Pr₀.₇Fe₇₅.₄Co₂.₅B₈.₈Zr₂.₆Ti₂.₅ magnets. A dimensionless process mapping approach was applied to optimize energy input and minimize defect formation, enabling identification of process windows that result in high-density (95 - 99%) magnets. This work explores how key process parameters such as point distance (15 – 60 µm), layer thickness (40 and 60 µm), and laser beam diameter (70 µm, focused vs. 120 µm defocused) can be optimized. The results showed that reducing point distance to a moderate range, along with reduced layer thickness and a wider defocused beam, led to lower volumetric energy densities and improved magnetic performance. Notably, higher density alone did not always correlate with superior magnetic properties. A remanence of 0.51 T and a coercivity of 673.22 kA/m were achieved. Furthermore, the optimized parameters were successfully used to fabricate complex-shaped demonstrators, showcasing the potential of PBF-LB for producing dense, structurally sound magnetic components with intricate geometries. The results highlight PBF-LB as a competitive alternative to traditional magnet fabrication methods, offering a viable pathway for the next generation of energy-efficient and sustainable magnetic materials.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".