Behavior of conventional and new PM lubricants as a function of processing parameters
Bibliographic record
Abstract
The number of complex PM parts for high performance appilcation continuously increases. Compaction is one of the key processes to reach such high performances and lubricants play a key role in that regard. This explains in the significant level of efforts, in the last 20 years, devoted to the development of PM lubricants to improve both the processing and performances of PM parts (density, ejection, robustness, productivity, etc...). FOr instance, warm pressing, high green strength, high density as well as flow enhanced formulations were - and still are - being developed. The development of these lubricants requires the understanding of the lubrication mechanisms of these organic organometallic compounds. In this paper, the effect of different compacting processing parameters (pressure, temperature, compaction speed) on the lubrication behaviour of iron-based powder mixes compacted on either an instrumented laboratory press or an industrial mechanical press will be reviewed and presented as a function of different families of PM lubricants (conventional, High Density, High green strength, warm die (50-80ºC) and warm pressing (100-150ºC)).
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 | 0.001 | 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".