Quality Index Charts of Al-Si-Mg Semi Solid Alloys Subjected to Multiple Temperatures Aging Treatments and Different Quenching Media
Bibliographic record
Abstract
The use of quality index charts is considered as an effective mean for evaluating the mechanical performance of Aluminum alloys for industrial engineering applications. The current study was carried out to investigate the influences of multiple-interrupted temperatures aging and quenching media (water versus air) on the quality index performance and precipitations evolution of A357 Aluminum semi solid alloys. Regarding the lack of similar investigations applied on such alloys, the quality index charts were generated for Al-Si-Mg semi solid castings based on its tensile properties. These charts are used to determine the quality index, in MPa, as a simple mean for compromising the strength and ductility together in one value using the Drouzy model. The multiple temperatures aging cycles were applied to improve the quality index values of Al-Si-Mg semi solid alloys for enhancing its characteristic and performance to resist the mechanical failures relating to automotive dynamic parts. The evolution of Mg2Si hardening precipitates, formed for specific thermal aging cycles, was investigated using transmission electron microscopy (TEM). The results obtained in this work revealed that the optimum quality index values were obtained by the application of T6-thermal under-aging treatment cycles. The regression models, using a statistical design of experiments, indicated that the optimum strength and high-quality index values were obtained by the application of interrupted thermal aging cycles, mainly C2,3-T6/T4/T7 conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".