Bibliographic record
Abstract
With the expanding demand on application of magnesium alloys in automotive and aerospace industries, robust methods in fatigue characterization of commercially available magnesium alloys with high specific strength is anticipated. In this paper, rotating bending load controlled tests has been studied on specimens machined from an extrusion piece of AZ31B. Due to asymmetric and anisotropic behavior of AZ31B, methods of transferring the load controlled results into stress-life results are lacking. Using the approach of variable material property (VMP), the load-stress relation in fully reversed test is derived. To apply the proposed method to AZ31B, the cyclic behavior of the material is required. Fully reversed step-loading of AZ31B over a wide range of strain amplitude was carried out on a servo hydraulic fatigue rig and was reported in the literature. The stabilized cyclic behavior at half-life was obtained for different load levels. Also, the cyclic tension and the cyclic compression curves were obtained. Using the proposed loadstress model and the actual cyclic behavior of AZ31B, an elastic-plastic solution over the whole domain of cross section was obtained. An energy based fatigue model was then employed to predict the life. The predicted and experimental lives agree well. The proposed method is recommended for correlating the load controlled test with stress and/or strain controlled 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 | 0.001 | 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".