Bibliographic record
Abstract
Die cast aluminum wheels are one of the most difficult automotive castings to produce because of stringent cast surface and internal quality requirements. As part of a collaborative research agreement between researchers at the University of British Columbia and Canadian Autoparts Toyota Inc., work has been underway to predict heat transport and porosity formation in die cast A356 wheels. Preliminary work has focused on assessing a number of criteria functions previously proposed in the literature. Model results in the form of temperature and criteria function predictions are compared with experimentally measured temperature and porosity data obtained from a directionally chilled A356 aluminum alloy solidified under conditions resembling those found in an industrial die-casting operation. The results suggest that the Niyama function is best suited to qualitatively predict porosity of the four criteria functions examined. However, all criteria functions, including the Niyama, do not appear to be well suited to predict the amount of porosity quantitatively as they fail to include the effect of varying hydrogen and inclusion content. A 2-D axisymmetric mathematical model incorporating flow through the interdendritic network and the thermodynamics of hydrogen solubility has been developed. This model has been successfully applied to the prediction of the amount of porosity in a series of test castings with varying hydrogen content.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".