Toward the development of a thermal-stress model of an industrial counter pressure casting process
Bibliographic record
Abstract
Abstract It is argued that the Counter Pressure Casting (CPC) process is superior over low-pressure die casting (LPDC) in terms of reducing defects and improving cast product performance. To date, there has been relatively little research conducted on the CPC process to provide reliable data to confirm this argument. In this work, a plant trial has been done on an industrial CPC process using a custom-designed ‘H-shaped’ die to acquire an extensive amount of quantitative process data. The data acquired includes temperatures obtained from within the die, the casting, the surrounding environment, and within specific die cooling channels. The data has been processed and analysed to support the development of a comprehensive thermal-stress model of the casting process in order to better understand and quantify the essential macro transport processes. This paper presents a methodology of a coupled thermal-stress model development on the CPC process, and the preliminary results obtained from the models. The results to-date clearly show the need for a fully coupled thermal-stress analysis for the particular casting geometry and process conditions examined. Some of the challenges associated with the current modelling approach are also identified and potential solutions presented.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".