Modeling of the Influence of Admixing and Prealloying on the Optimisation of Compressibility and Sinter-Hardenability of Steel Powders
Bibliographic record
Abstract
The automotive industry applies pressure on the PM industry to produce components with superior mechanical properties at minimum cost. In this regard, sinter-hardenable powders are particularly well suited since they allow direct quenching of components at the end of the sintering cycle, thus eliminating the extra steps required for heat treating. This paper presents the results of the modeling of the influence of admixing and/or prealloying on the optimization of compressibility and hardenability of sinter-hardenable steel powders. A first design of experiments (DOE) was used to optimize the chemical composition and to study the interactions between prealloyed elements (Nickel, Chromium, Molybdenum and Manganese) and admixed elements (Nickel, Chromium, Manganese and Copper) on hardenability and compressibility. A second DOE was generated based on the results obtained in the first series. Results show that among all of the examined alloying elements, only prealloyed nickel, chromium and molybdenum had a significant effect on compressibility and hardenability. Moreover, within the range of concentrations under study, the optimum sinter-hardenable powder had the following (prealloyed) chemistry: 1.5 wt-% Ni, 0.55 wt-% Cr and 1.25 wt-% Mo.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".