Rheology of SS-316L metal powders exposed to laser powder bed machine cycles: an interlaboratory study
Bibliographic record
Abstract
Powder spreadability significantly affects the reliability and productivity of various additive manufacturing (AM) processes, including laser-beam powder-bed-fusion (PBF-LB) processing. Part quality relies on the uniformity of the powder layer density within a build and on the reproducibility between builds. Moreover, the overall process productivity is affected by the capability to spread the powder layers quickly and uniformly; both are highly dependent on the powder flowability. The correspondence between powder performance in powder-bed-fusion machines and powder flowability measured with standard methods (MPIF,1 ATSM2 and ISO3) is not always clear and the America Makes & ANSI AMSC Standardization Roadmap for Additive Manufacturing4 recently reported that existing standards for flowability do not account for the range of conditions that a powder may encounter during AM processes. Consequently, there is a necessity to develop and validate other characterization methods adapted for the specific needs of additive manufacturing. Powder flowability can be influenced by environmental and handling conditions such as humidity, temperature, and atmosphere. Powder flowability is also affected by many powder characteristics such as the density, the particle-size distribution (PSD), and morphology, the presence of satellites, and surface characteristics. As most of these characteristics can be modified during the AM build and recycling processes, monitoring the powder behavior is essential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".