Application of a mathematical model to multipass hot deformation of aluminium alloy AA5083
Bibliographic record
Abstract
Sheet metal forming operations are used extensively in industry to alter the shape of the metal through plastic deformation. A critical step in the sheet manufacturing process is hot rolling which reduces the thickness of the ingot and can significantly impact the final sheet properties based on the microstructure evolution during this operation. A two-dimensional mathematical model was developed and experimentally validated to simulate deformation and microstructure evolution during multipass hot rolling for an AA5083 aluminium alloy. The details of model development and experimental validation can be found in earlier work. In this article, the application of the validated model to further understand and optimise the material stored energy and ensuing microstructure during multipass hot rolling is described. Specifically, the model was employed to examine the effect of changing the number of rolling passes as well as strain partitioning during multipass rolling on the material stored energy and the resulting microstructure. Results indicate that the number of passes has a significant effect on the stored energy which increases as the number of passes increases. In addition within a multipass rolling schedule the way in which the strain is partitioned is also shown to have an effect on the stored energy with a decreasing strain/pass schedule providing the highest material stored energy after rolling is complete. In contrast an increasing strain/pass schedule provides the lowest stored energy in the material after rolling. This overall effect is attributed to the differences in strip temperature as the lowest exit temperature strip has the highest stored energy. The model was further utilised to generate operational curves to predict the material stored energy and subsequent recrystallisation under different rolling conditions, namely at different interpass times and total strains for various start deformation temperatures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".