Mechanical characterisation and quality index of A356-type aluminium castings heat treated using fluidised bed quenching
Bibliographic record
Abstract
The current study aimed at investigating the effect of fluidised sand bed quenching on the mechanical performance and quality index of A356·2 aluminium cast alloys. Traditional water and conventional hot air quenching media were used to establish a relevant comparison with fluidised sand bed quenching. Quality charts were generated using two models of quality indices to support the selection of material conditions on the basis of the proposed quality indices. The use of a fluidised sand bed for the direct quenching aging treatment of A356·2 casting alloys yields greater UTS and YS values compared to conventional furnace quenched alloys. For the same aging conditions (170°C/4 h), the fluidised bed quenched aged 356 alloys show nearly the same or better strength values than those quenched in water and then aged in a CF or an FB. Based on the quality charts developed for alloys subjected to different quenching media, higher quality index values are obtained by water quenched T6-tempered A356 alloys. Using hot sand as a quenching medium at different temperatures, namely 170, 190 and 210°C, reduces both the strength and the quality with increase in quenching temperature for the alloys investigated. The regression models indicate that the eutectic silicon modification factor has the most significant effect on the quality results of the alloys investigated, for all heat treatment cycles, as compared to other metallurgical parameters.
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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 | 0.001 | 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".