Powder flowability and density: effect of humidity and impact on the reproducibility of the measurements
Bibliographic record
Abstract
Additive manufacturing community recognizes that better powder flowability leads to better final products. However, there is presently no agreement on what constitutes a good flowability. Indeed, AM machine users have reported that powders appearing to be identical may exhibit different spreading behavior in the machines. Flowability is not an intrinsic property and depends on many different factors including the conditioning of the powder, the measurement method and the environmental conditions during the tests. Consequently, significant variations have been reported from laboratory to laboratory but also within a same laboratory when using standard procedures to measure flow. Thus, there is a need to better understand flowability and develop reliable and relevant methods to qualify powders. One of the important factors affecting the flowability is the relative humidity. However, the impact of humidity on the flowability of powders for additive manufacturing has not been well documented and current standards are not taking this effect into account. This paper presents the impact of humidity on the flowability of titanium powders using various tests (Hall, Carney, apparent density, angle of repose, rheology, avalanche, spreadability). While the effect of humidity is significant, not all methods have the same sensitivity to the level of humidity. The paper also presents methods to measure the level of humidity and the effect of measurement method on the results.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".