Cyclic hysteresis of AZ31B extrusion under load‐control tests using embedded sensor technology
Bibliographic record
Abstract
Abstract The fatigue behaviour of AZ31B extrusion magnesium alloy under load‐control cyclic test conditions is estimated using a combination of simulation and experimental results. The strain measurement of this asymmetric material is found experimentally using a Fibre Bragg Grating (FBG) sensor during rotating bending tests. Then, to analyse applied stresses in the sample – particularly in the plastic deformation range – the Variable Material Property (VMP) method is employed. Using this simulation method, the hysteresis loops of two critical top and bottom elements of the sample's cross section under different bending moments are obtained. Finally, the strain of the sample during rotating bending, as measured by the embedded FBG sensor, is related to the stresses obtained from the modeling using a mapping function. The hystereses obtained from this combination of the modeling and experimental results are compared with the results of a companion strain‐control pull–push test in which the input strain history was that of measured by the FBG sensor. Observations verify that the stresses of the combined VMP‐FBG hysteresis loops have good compatibility with the stress responses obtained through the experiment. The hybrid model introduced in this work can be employed to capture cyclic hysteresis, and hence estimate the fatigue life, under load‐controlled rotating bending 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".