A Study of Bubble Entrainment as Related to Runner Velocity in Aluminum Sand Castings Using the Cosworth Process
Bibliographic record
Abstract
Traditional gravity pour down-sprue methods of filling moulds in the making of aluminum castings inherently lead to oxide and air bubble entrainment. The reason for this is found in the high velocities the metal flow experiences during the filling of a mould. The Nemak Windsor Aluminum Plant (WAP) produces cylinder blocks using the low-pressure Cosworth process, which includes low velocity up-hill filling of the sand mould package. This doctrine is followed in all except one part of the process: the runner system. The nature of the resulting defect is generally known as Head Deck Porosity. Runners were cast full in open production runners at three different velocities with the resulting quickly chilled castings analyzed using X-ray radioscopy, and Scanning Electron Microscopy. Results reveal that the subject bubble porosity is indeed the result of air entrained during initial transient flow within the production runner system whose velocity is higher than the critical value of 0.5ms-1. This theoretical value is corroborated by experimental results. In addition, a new "sessile" runner of optimized shape, filled at a velocity slower than the critical value, is proposed and analyzed using Magmasoft mould fill modelling software. The design can potentially replace the existing runner providing a casting free of entrained air.
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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 | 0.000 | 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) | 0.000 | 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".