Experimental and Numerical Replication of Thermal Conditions in High-Pressure Die-Casting Process
Bibliographic record
Abstract
Acquiring reliable thermal data during the high-pressure die-casting (HPDC) process remains a significant challenge due to its complexity and rapidly evolving thermal environment. In industrial settings, the influence of process parameters is typically evaluated after solidification by examining the final casting quality, as direct temperature measurements within the die during operation are difficult to obtain. Additionally, most casting simulation tools lack accurate correlations for the interfacial heat transfer coefficient (IHTC) as a function of process parameters. To address this limitation, a laboratory-scale hot chamber die-casting (HCDC) apparatus was developed to replicate the fluid flow and the thermal conditions of industrial HPDC operation while enabling direct thermal measurements inside the die cavity using embedded thermocouples. The molten metal temperature was estimated using the lumped capacitance method, and the IHTC was determined through a custom inverse heat conduction algorithm incorporating an adaptive forward time-stepping scheme. This algorithm was validated by solving the forward heat conduction problem using the ANSYS 2025 R1 Transient Thermal solver. The experimentally obtained IHTC values showed good agreement with those measured during industrial HPDC trials, with a maximum deviation of about 14% in the peak value, while the full width at half maximum (FWHM) differed by less than 12%. These results confirm that the developed HCDC setup can reliably reproduce industrial thermal conditions and generate high-quality thermal data that can be used in numerical casting simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".