Characterisation of the mechanisms taking place during liquid phase sintering of PM boron steels with the help of artificial intelligence
Bibliographic record
Abstract
Liquid phase sintering (LPS) of powder metallurgy (PM) components is a well-recognised strategy to enhance the densification of pressed-and-sintered compacts. This work reports the investigation on the liquid phase formation when a Fe–Ni–Mn–C–B master alloy (MA) is used as a boron carrier in combination with two iron base powders pre-alloyed with Mo. Through differential scanning calorimetry tests, quantitation of the microstructure with the help of artificial intelligence, as well as measurement of sintered density and strength as a function of sintering temperature, it was possible to unravel the mechanisms that take place before and during LPS. It was confirmed that a cascade of events takes place in the solid state prior to reaching the temperature necessary for a eutectic reaction to form a liquid. Additionally, the pre-alloyed Mo content was identified as a factor that modifies the initiation of LPS but not the LPS mechanisms per se.
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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 | 0.000 | 0.000 |
| 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".