Bibliographic record
Abstract
In the past decade, magnesium (aluminum) alloy use in the automotive industry has increased in order to reduce vehicle weight and fuel consumption. However, their applications are usually limited to temperatures of up to 120°C. Improvements in the high-temperature mechanical properties of magnesium alloys would greatly expand their industrial applications. As compared to the unreinforced monolithic metal, metal matrix composites have been recognized to possess superior mechanical properties, such as high elastic modulus and strengths as well as enhanced wear resistance. In this study, a novel approach of making hybrid preforms with two or more types reinforcements, i.e., different size particles and fibers, for magnesium-based composites was developed. An advanced and affordable technique of fabricating hybrid magnesium-based composites called the preform-squeeze casting was employed successfully. The microstructures of the hybrid preform and the reinforced magnesium-based composites were analyzed with both optical microscopy (OM), and scanning electron microscopy (SEM). The microstructural analyses show that both the particles and fibers distribute homogeneously in the preform and the composite's matrix material. The engineering performance of the composites was evaluated by tensile testing both the reinforced and unreinforced matrix alloy. The tensile testing results indicate that the tensile properties of the composites are considerably improved compared with those of the matrix magnesium alloy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".