Calculation and validation of heat transfer coefficient for warm forming operations
Bibliographic record
Abstract
In an effort to reduce the weight of their products, the automotive industry is exploring various hot forming and warm forming technologies. One critical aspect in these technologies is understanding and quantifying the heat transfer between the blank and the tooling. The purpose of the current study is twofold. First, an experimental procedure to obtain the heat transfer coefficient (HTC) as a function of pressure for the purposes of a metal forming simulation is devised. The experimental approach was used in conjunction with finite element models to obtain HTC values as a function of die pressure. The materials that were characterized were AA5182-O and AA7075-T6. Both the heating operation and warm forming deep draw were modelled using the LS-DYNA commercial finite element code. Temperature-time measurements were obtained from both applications. The results of the finite element model showed that the experimentally derived HTC values were able to predict the temperature-time history to within a 2% of the measured response. It is intended that the HTC values presented herein can be used in warm forming models in order to accurately capture the heat transfer characteristics of the operation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".