Developing castability index for magnesium diecasting alloys
Bibliographic record
Abstract
A partial castability index has been developed for magnesium diecasting alloys based on alloy characteristics such as solid thermal conductivity, non-equilibrium freezing range and hot tear sensitivity. The partial castability index I C for thin walled castings was developed using an industrial diecasting defect index I DD of five different alloys and regression analysis to give I C −0·08(ΔF)+0·58Δκ+0·01ΔHTS+0·06ΔT′ with R2=0·9977, where ΔF=(freezing range for AZ91)−(freezing range for alloy); Δκ=(thermal conductivity of alloy)−(thermal conductivity of AZ91); ΔHTS=(hot tear sensitivity of AZ91)−(hot tear sensitivity of alloy); ΔT′=(non-equilibrium freezing range of alloy)−60°C. The hot tear test and its hot tearing susceptibility (HTS) rating determined using a constrained rod casting mould can also assess the trend in diecastabilty of magnesium alloys in thin walled castings but the prediction is not as good as the partial diecastability index I C. The HTS rating correlates with the industrial diecasting defect index I DD as I DD=0·9HTS0·5.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".