Distribution of the Quality Index in AlSiMg Castings Produced by Different Processes: Reality vs Prediction
Bibliographic record
Abstract
The Quality Index is a useful tool allowing to assess the metallurgical quality of aluminium AlSiMg foundry alloys (AlSi7Mg03/AlSi7Mg06, or A356/357 in North America). Its value, calculated from the ultimate tensile strength and elongation, increases with the metallurgical quality of the casting, i.e. with finer dendrites, lower microporosity, lower iron content and cleaner metal. This Quality Index does not depend on the degree of temper applied during the heat treatment (aging time and temperature). This paper will briefly explain how solidification modeling can provide the local value of the Quality Index, via the secondary dendrite arm spacing and level of microporisity calculated from the predicted values of the local solidification time and solidus velocity. The validity of the predictions has been assessed against the properties of tensile specimens excised from parts of different geometries produced by a variety of casting processes, as reported in the literature. The case study of a race car bell housing poured by two processes, namely gravity sand casting (GSC) and low pressure die casting (LPDC, or LPPM for Low Pressure Permanent Mold in North America), will be the focus of the present paper. The Quality Index has been measured and predicted at 3 locations in the castings after a T6 temper was applied which resulted in a minimum elongation of 5% for both the GDC and LPDC bell housings. Metallographic examination and image analysis allowed to quantify the superior metallurgical quality of the LPDC housings when compared to their GDC competitors.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".