Metal powder atomization preparation, modification, and reuse for additive manufacturing: A review
Bibliographic record
Abstract
Additive manufacturing (AM) processes are pivotal in various manufacturing industries due to their efficiency and ability to produce parts with complex structures and shapes. Metal powders, essential as feedstock for AM, especially in direct energy deposition (DED) and powder bed fusion (PBF) processes, have garnered significant attention from academia and industry. However, a comprehensive review focusing on the entire lifecycle of powders for AM is currently lacking. This review provides an exhaustive overview of powders used in AM, covering powder preparation methods, modification, and reuse. We critically discuss and compare various powder preparation techniques and review their properties, characterization methods, and impacts on AM processes. Here, we also summarize powder modification methods and improvements in powder properties and AM-produced parts. Finally, we address the reuse of powders in AM fabrication, including strategies, effects, and assessments of reusability post-manufacturing, which are crucial for reducing AM-associated costs. This work offers a state-of-the-art perspective in preparation, modification, and reuse of metal powders in AM.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".