Determination of optimal location to monitor temperature in low pressure die casting process
Bibliographic record
Abstract
Controlling and eliminating defects, such as macroporosity, in castings is a continuing challenge that manufacturers must continually address. Since the encapsulation of liquid regions by a solid shell and subsequent formation of macroporosity cannot be detected during casting, the die temperature, which is routinely measured, has been used as an indirect indicator of this defect. A finite element model has been developed to predict the evolution of temperature as well as the volume of encapsulated liquid in a casting with a high propensity to form macroporosity. The boundary conditions in the model were iteratively adjusted until the temperature predictions matched the experimental data for a variety of operational conditions. A model based methodology has been developed to analyse the correlation between the die temperature and the encapsulated liquid volume. This methodology is employed to assess the suitability of different in-cycle die temperatures for use as indicators of macroporosity formation, and to help determine the optimal location to monitor temperature for the purpose of minimising macroporosity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".