Process parameters and modeling features affecting interfacial heat transfer coefficients in the casting of a semi-solid 357 aluminum alloy
Bibliographic record
Abstract
A study was carried out to identify process parameters and modeling features affecting interfacial heat transfer coefficients in the casting of a semi-solid 357 aluminum alloy. A 2-level screening design was created to evaluate the effect of 5 process parameters on these coefficients. The varied parameters were the following: set die temperature, intensification pressure, plunger velocity, lubricant type and lubricant quantity. The interfacial heat transfer coefficients were calculated with an inverse heat conduction method using as inputs the measured die temperatures obtained from the various casting conditions in the screening design. The effects of these process parameters were quantified considering 2 response variables: the peak value of the coefficients and the time the coefficients decreased to 5 % of their peak value. The intensification pressure was found to be dominant for both response variables. It was also observed that features in the finite element model used to solve the inverse heat conduction problem affected the coefficients. The features investigated in the model were: the thermocouple cavity, its depth, its base angle and the presence of the thermocouple inside the cavity. The depth at which the thermocouple cavity was modeled was found to be the prevailing feature affecting the coefficients.
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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".