Experimental study on high temperature tensile behaviour of aluminium alloy AA5083 with oscillating load
Bibliographic record
Abstract
The flow behaviour of aluminium alloy AA5083 at 450 [Formula: see text]C has been investigated under quasi-static loading conditions with and without a superimposed oscillating load. Samples were placed under tensile load at constant strain rates ranging from 0.001 to 0.3 s[Formula: see text]. A fixture was designed to generate the required sine-wave oscillation and was attached to the MTS tensile test machine along with a secondary, highly sensitive load cell. The frequencies of the imposed oscillations ranged from 5 to 100 Hz with an amplitude ranging from 0.02 to 0.5-N. It was observed that the imposition of oscillations influences the deformation behaviour of the material. Although the yield and tensile strength remain relatively constant, the total elongation is 8-23% higher under an imposed oscillating load. In addition, the thickness distribution profiles along the gauge length of the tensile specimens were investigated and it was observed that in the presence of oscillations the thickness distribution is more uniform. It was concluded that the presence of a superimposed oscillative load will result in greater deformation capabilities before fracture and postpone the occurrence of damage compared to conventional forming. This phenomenon was further explored utilising a user-defined material subroutine developed for the finite element solver LS-DYNA to simulate the conducted constant load tensile tests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".