Effect of Superheating Melt Treatment on Mg 2 Si Particulate Reinforced in Al-Mg 2 Si-Cu In situ Composite
Bibliographic record
Abstract
As one of the advanced engineering materials, Al-based composite reinforced with Mg 2 Si phase has been assigned to be a potential candidate in the manufacture of automotive products, especially those that require high temperature applications. Superheating melt treatment with various temperatures and holding times are shown to cause alteration of primary Mg 2 Si reinforced particulate that subsequently would improve the mechanical properties of the in situ composite. This was investigated via microstructural and thermal analysis observation. Superheat temperature at 950 °C with 15 minutes holding time has presented an adequate modification effect with skeleton structure of Mg 2 Si particles faded and transformed into fine polygonal shape, accompanied with decrease in size. In addition, the thermal analysis result has shown increment in nucleation temperature, T N compared to unmodified composite indicating the modification of particles is allocated with this composite melt treatment. This modification of structure is believed capable to enhance the strength properties of the in situ composite that could meet the application requirements.
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 | 0.000 |
| 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".