Mathematical modelling of deformation and microstructure evolution during hot rolling of AA5083 aluminum alloy
Bibliographic record
Abstract
Hot rolling, a critical process in the manufacturing of aluminum sheet, can significantly impact the final properties of the cold rolled sheet. The control of the thermo-mechanical conditions during hot rolling and the resulting microstructure of aluminum sheets is critical in order to determine the final sheet properties. Several complexities are associated with controlling microstructure evolution especially during multi-pass hot rolling. Firstly, the microstructure evolution is a result of the complex interaction between the deformation history that the material experiences during rolling and the resulting material changes that occur during rolling. Moreover, the multi-pass aspect of the rolling process adds to the complexity of the process as the prior thermomechanical history can influence the material stored energy and response to subsequent deformation. This calls for further understanding of the way the stored energy is accumulated in situations where various recrystallizations levels may occur in the interpass region to be able to follow and track microstructure changes. In this research, a comprehensive mathematical model has been developed to predict through-thickness thermal and deformation history of AA5083 aluminum sheet undergoing single-pass and multi-pass hot rolling using the commercial finite element package, ABAQUS. A physically based internal state variable microstructure model was employed to calculate the material stored energy and subsequent recrystallization kinetics as a function of deformation conditions. A new more physically-based approach to account for the non-isothermal cooling in the inter-pass region was applied to capture and track the accumulation of the material stored energy during multi-pass hot rolling. The model has been extensively validated against experimental measurements conducted using Corns' pilot scale rolling facility located in IJmuiden, the Netherlands for AA5083 aluminum alloy sheet under a wide variety of industrially relevant single-pass and multi-pass hot rolling conditions. The model was able to predict the temperature, strain profile and the rolling load reasonably well for both single-pass and multi-pass rolling cases. The model was able to predict the fraction recrystallized relatively well for all the cases. The model predicted recrystallized grain size was in reasonable agreement with the measurements for single-pass rolling cases while a constant deviation of ~ 11 μm was observed for multi-pass ones. The validated model was further utilized to determine the sensitivity of the predicted fraction recrystallized to changes in the rolling process. The model was also applied to achieve further understanding and optimization of the rolling process such as the way the strain is partitioned. The results indicate the merit and usefulness of the model as a powerful tool to further understand the complex interactions between the thermo-mechanical and microstructure changes during rolling and thus achieve process optimization and control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".