A novel phenomenological material model and calibration for high temperature material behaviour of AA5083
Bibliographic record
Abstract
With the constant prioritization for vehicle lightweighting, high temperature forming processes are increasing in demand due to their capability to produce large and complex parts in a single forming operation. Superplastic forming is utilized to form such parts, but this process typically requires long forming times. To remedy this, an increasing number of processes are being developed which utilize a higher and more variable strain rate history during the forming process. As a result, there is a growing need to develop material models capable of simulating high-temperature forming processes characterized by variable strain rates and different deformation mechanisms acting at different strain rates. This study proposes the utilization of physical based modelling concepts to construct a simple, phenomenological model that is easy to use within industry. Consequently, tensile tests were conducted on aluminum alloy AA5083 at temperatures of 400°C, 450°C and 500°C, at strain rates ranging from 0.0005 s −1 to 0.15 s −1 . Additionally, an iterative model parameter calibration procedure is proposed, verified, and validated with LS-DYNA finite element simulations to achieve accurate predictions of material behaviour all the way up to the onset of localized necking. The generated material model(s) yielded validation metrics greater than 96% for the investigated data sets. The accuracy of the model was further assessed using tensile testing data with changing strain rates, yielding validation metrics on average greater than 90%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".