Effect of atmospheric humidity and temperature on the flowability of lubricated powder metallurgy mixes
Bibliographic record
Abstract
Good powder mix flowability is required in high-volume powder metallurgy (P/M) manufacturing in order to ensure a uniform and consistent filling of the die cavities and, in turn, high productivity, low rejection rates, part integrity and consistent part-to-part characteristics such as dimensional change. Apart from the particle size and the shape of the metallic powders and additives, lubricants, even though admixed in small quantities (<1.5 weight %), have a significant impact on the flow characteristics of powder lend formulations. In addition to that, the blending parameters and the atmospheric conditions such as powder temperature and atmosphere temperature and humidity, may also affect significantly the flow of powder mixes. For instance, it is know that some mixes produced during humid and hot summer days may behave differently. In this paper, the effect of relative humidity and temperature on the flow of conventional P/M mixes containing different types of lubricants was studied. Typically, amide wax, Kenolube, zinc stearate as well as proprietary lubricants were admixed in a V-blender enclused in an environmental chamber set at different humidity and temperature levels to simulate different atmospheric and processing conditions. The sensitivity to such conditions of the different lubricants was assessed and the influence on the flow of the powder mixes as well as on the compaction and ejection behaviors was measured.
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 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".