Effect of Temperature, Heating Rate, and Cooling Rate on Bonding and Nitriding of AlSi10Mg Powder Occurring During Supersolidus Liquid-Phase Sintering
Bibliographic record
Abstract
This study investigated the effect of supersolidus liquid-phase sintering conditions on the powder particle bonding and the AlN-phase formation of an AlSi10Mg alloy. Sintering was conducted at temperatures between 550 and 579 °C, with a holding duration of 2 h under a nitrogen atmosphere. The sintering cycles included four heating segments, performed at rates ranging from 0.2 to 5 °C/min for a total of between 5 and 15 h, and a cooling segment performed at two different cooling rates, 0.15 and 5 °C/min, resulting in durations of 12 and 70 h, respectively. Three powder batches exhibiting different particle size distributions were tested. An X-ray diffractometer, optical microscopy, and scanning electron microscopy were used to characterize phase formation and particle bonding. The results show that higher sintering temperatures and faster heating/cooling rates led to a lower fraction of AlN. In contrast, lower sintering temperatures or slow heating promoted the development of a thicker AlN shell around powder particles, inhibiting the bonding of the AlSi10Mg powder and preventing densification via the sintering process. These findings suggest that sintering at temperatures between 570 and 575 °C, with heating and cooling rates of at least 2 °C/min, constitutes a more favorable window for the densification of AlSi10Mg under a nitrogen atmosphere.
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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 | 0.001 |
| 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".