Mathematical model of deformation and microstructural evolution during hot rolling of aluminium alloy 5083
Bibliographic record
Abstract
A mathematical model to predict the through thickness temperature, strain and strain rate distributions during hot rolling and the subsequent microstructure evolution was developed using the commercial finite element package ABAQUS. Microstructure evolution predictions included the amount of recrystallisation through the thickness of the sheet based on its thermomechanical history during rolling and thermal history after rolling. The equations used to predict the microstructure evolution were based on semiempirical relationships found in the literature for a 5083 aluminium alloy. Validation of the model predictions was done using comprehensive experimental measurements which were conducted using the Corus research multimill, a pilot scale experimental rolling facility, in Ijmuiden, The Netherlands. The results indicate that the through thickness temperature and strain distribution predictions for the rolling operation are reasonable. Hence, the boundary conditions used in the finite element model adequately represent the interface heat transfer and friction conditions. Microstructure predictions using the literature based equations significantly underestimate the amount of recrystallisation occurring in the sheet. A sensitivity analysis indicates that the recrystallisation kinetics are extremely sensitive to the fitting parameters used in the microstructure equation, and that the gradient in the recrystallisation kinetics is the result of the temperature gradient experienced by the specimen during deformation.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".