A Critical Review of Mg–Zn–Y Series Alloys Containing I, W, and LPSO Phases
Bibliographic record
Abstract
Magnesium and its alloys are playing an increasingly important role in the transportation industry due to the pressing demand of lightweight structures to improve fuel efficiency and reduce CO2 emissions. But the shortcomings of poor room temperature (RT) formability and high temperature strength, tension–compression asymmetry, and anisotropy are among the major issues which are currently limiting their widespread applications. Recently, research and development activities of Mg–Zn–Y series alloys have significantly increased because of their superior mechanical properties arising from the formation of different ternary phases known as I (Mg3YZn6), W (Mg3Y2Zn3), and LPSO (Mg12YZn). In this review article, the crystal structure of these phases and their orientation relationships with hexagonal magnesium matrix are discussed. Recent advances about I, W, and LPSO phase containing Mg alloys are presented, along with the effects of these phases on the microstructural evolution and mechanical properties including tensile, compressive, fatigue, and creep resistance. Important aspects involving thermal stability, phase transformation, and influence of heat treatment are also described. Based on the current status, some existing issues are pointed out and further studies are suggested so as to warrant safe and reliable lightweight structural applications of Mg–Zn–Y alloys in the automotive industry.
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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 | 0.003 | 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) | 0.005 | 0.002 |
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".