Bibliographic record
Abstract
A single process cycle in high-pressure die casting involves a series of operations with complex flow, thermal, and metallurgical phenomena. Most commercial simulation software includes the mathematical framework to handle those. However, the user still needs to define a large number of coefficients to reproduce the magnitude of the physics and may also have to program functions to better replicate reality. This calibration effort can be an enabler for higher-fidelity simulations to efficiently develop new high-quality products, especially in the high-integrity field. This paper presents a data collection effort on a connected high-pressure vacuum die casting research and development cell that is fed into a process simulation. First, data was collected from the ladling robot, die casting machine, and spray actuator to provide an accurate representation of a typical cycle. Secondly, heat transfer behaviour was estimated for the cold chamber during pouring as well as for die spray to capture the heat exchanges in the system, as a complement to previously-acquired data for melt-to-die heat transfer. User functions were then programmed in ProCAST® to add the required features. Using this data, simplified cyclic simulations were run to calculate the expected steady-state die temperature regime and compare it to experimental data. Finally, a single-cycle flow and thermal simulation was run including the ladle pouring into the cold chamber. This works highlights some of the challenges and opportunities associated with a functional, off-line digital twin for high-pressure vacuum die casting, used as a tool to design new products and processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".