Comparative evaluation of Mo-Ni-Cu diffusion-alloyed, organic-bonded and conventional un-bonded steel powder mixes under industrial conditions
Bibliographic record
Abstract
Mo-Ni-Cu steel powders are widely used to manufacture high performance PM parts. A large proportion of these parts are produced from diffusion-alloyed powders, where Ni and Cu are partially diffused with steel powder to enhance bonding and chemical consistency. Binder-treatment technology such as the FLOMET™ process is a cost effective alternative to the diffusion-alloyed process that also ensures excellent Ni and Cu bonding. Recent developments in the FLOMET process have allowed the development of new Organic-Bonded powders offering Ni and Cu bonding strength similar to that of diffusion-bonded powders but with improved compressibility and chemical versatility. The compaction and ejection behaviour as well as the dusting resistance of Mo-Ni-Cu steel powder mixes produced with diffusion-alloyed, organic-bonded and conventional mixing were evaluated on an industrial press. The dusting resistance of each mix was determined by measuring the amount of dust (Ni, Cu, Fe and others) around the die cavity and on the press operator. The chemical and dimensional consistency of parts sintered in a fast cooling industrial furnace is also discussed.
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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) | 0.001 | 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".