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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".