Bibliographic record
Abstract
ABSTRACT Lightweighting in ground vehicles is today considered as one of the most effective strategies to improve fuel economy and reduce anthropogenic environment‐damaging and climate‐changing emissions. Magnesium (Mg) alloy, as a strategic ultra‐lightweight metallic material, has recently drawn a considerable interest in the transportation industry to reduce the weight of vehicles due to their high strength‐to‐weight ratio, dimensional stability, good machinability and recyclability. However, the hexagonal close‐packed crystal structure of Mg alloys gives only limited slip systems and develops sharp deformation textures associated with strong mechanical anisotropy and tension–compression yield asymmetry. For the vehicle components subjected to dynamic loading, such asymmetry could exert an unfavourable influence on the material performance. This problem could be conquered through weakening the texture via addition of rare‐earth (RE) elements. Thus, a number of RE‐containing Mg alloys have recently been developed. To guarantee the structural integrity, durability and safety of highly loaded structural components, understanding the characteristics and mechanisms of cyclic deformation and fatigue fracture of such RE‐Mg alloys is of vital importance. In this review, the available fatigue properties including stress‐controlled fatigue strength, strain‐controlled cyclic deformation characteristics and fatigue crack propagation behaviour are summarized, along with the microstructural change and crystallographic texture weakening in the RE‐containing Mg alloys in different forms (cast, extruded and heat‐treated states), in comparison with those of RE‐free Mg alloys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".