Cold-Sprayed Ni and NdFeB-Al Powders Recovery and Reuse
Bibliographic record
Abstract
As cold spray additive manufacturing matures, significant efforts are being made to develop spray conditions for more challenging materials, thereby expanding the technology's range of applications. One main challenge while using commercially available equipment is that, even under optimized conditions, deposition efficiency remains low for some materials. Powder particles that do not adhere are wasted, which can severely affect the process economics, especially in a mass production context and/or when expensive feedstocks are used. Powder recovery and reuse is a logical solution to mitigate this problem, yet few studies evaluate its feasibility and its impact on powder characteristics and ultimately coating performance. In this work, powder recovery was investigated for two cases: a Ni powder and a NdFeB-Al powder mix, used respectively for repair applications and for the fabrication of permanent magnets. A prototype recovery system was built, achieving a recovery efficiency of up to 75%. The powders were recovered after up to four spray runs, and their morphology and size distribution were characterized. The magnetic properties of both powders and coatings were evaluated using hysteresis measurements. Although the process affects the particle size distribution and their magnetic properties, powders remain suitable for re-deposition for both materials. In particular, it was shown that NdFeB-Al mix maintains 97% of its initial magnetic performance under industrial operating conditions.
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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 | 0.000 | 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) | 0.000 | 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".