Models for symmetric cold rolling of an aluminum sheet
Bibliographic record
Abstract
In the current work, the behavior of Al alloys during cold rolling is studied with the help of numerical approaches such as Finite Element (FEM) and Flow-Line (FLM) Models. The applicable simplifications for each method have been summarized in this contribution. For simulating the process of rolling, a material model was employed, which is based on the measured values obtained from the tensile test. The results of the conducted rolling experiments were compared with the numerical simulations performed by employing the experimental material models. The analysis of simulated and experimental data allowed us to evaluate the friction coefficient. A relationship has been established between the minimum friction coefficient necessary for rolling and the estimated one and this result is in a good agreement with the counterpart reported in literature sources. The established method was used for the evaluation of the characteristic components of the strain, namely the normal, shear, and equivalent components. The comparative study between recorded measurements and simulations indicates that both the FEM and FLM models can be successfully applied to simulate the symmetric cold rolling process of aluminum with sufficient accuracy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".