Rare-earth containing magnesium alloys: A review of precipitation behavior and its impact on fatigue performance
Bibliographic record
Abstract
• The precipitation behavior in Mg-RE alloys and the critical role of precipitates in fatigue behavior are reviewed. • The alloying elements and heat treatment play a pivotal role in the precipitation behavior of Mg-RE alloys. • The size, quantity, and distribution of precipitates can be controlled through the optimization of aging treatment. • The effects of precipitates on HCF and LCF conditions are comprehensively reviewed. • Precipitates enhance fatigue resistance through dislocation pinning, crack deflection, precipitation strengthening, and twinning suppression. Magnesium (Mg) alloys have attracted considerable attention in the automotive and aerospace industries due to their exceptional lightness, high specific strength, and excellent castability. However, their susceptibility to fatigue failure poses significant challenges for the long-term service under cyclic loading. This review systematically explores the precipitation behavior in the representative rare-earth containing magnesium (Mg-RE) alloys and examines the critical role of precipitates in influencing fatigue behavior. The alloying elements and heat treatment play a pivotal role in affecting the precipitation behavior of the Mg-RE alloys. Notably, the β', β'', and 14H long-period stacking ordered (LPSO) phases serve as primary strengthening precipitates in the Mg-Gd (Y), Mg-Nd, and Mg-RE-Zn based alloys, respectively. The size, quantity, and distribution of these precipitates can be finely controlled through the optimization of aging treatment parameters. Based on the fundamental principles for enhancing fatigue resistance, this review offers a detailed analysis of the effects of precipitates on fatigue behavior, addressing key aspects such as crack initiation, propagation, and fatigue failure under high-cycle fatigue (HCF) conditions. Besides, the effects of precipitates on the cyclic stress response, cyclic deformation characteristics, and fatigue life under low-cycle fatigue (LCF) conditions are systematically summarized. The influence of precipitates on fatigue behavior of Mg-RE alloys is primarily attributed to the mechanisms such as dislocation pinning, crack path deflection, precipitation strengthening, and the suppression of twinning. This review highlights the significance of precipitation behavior in optimizing fatigue resistance and provides valuable insights into future research directions for advancing Mg-RE alloys in the fatigue-critical structural applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".