Investigating the Synergistic Impact of Cenosphere and Mg-Sn Alloy on the Tribological and Mechanical Properties of Aluminum Foam Composites
Bibliographic record
Abstract
The objective of this investigation is to enhance the mechanical and tribological properties of aluminum foam composites through the incorporation of cenospheres and a Mg-Sn alloy.Cenospheres, lightweight ceramic microspheres, are integrated as fillers within the metal matrix composites, capitalizing on their high strength-to-weight ratio and buoyancy.The synergistic effect of the Mg-Sn alloy addition is postulated to fortify the composite, augmenting its strength.These lightweight yet robust composites are poised to offer significant benefits in sectors demanding high performance and reduced weight, such as aerospace, automotive, and biomedical engineering.A meticulous examination of density, hardness, friction coefficients, and wear rates was conducted.It was observed that the inclusion of cenospheres precipitated a decrease in density from 2.51 to 2.01 g/cm 3 with a volume fraction increase from 0 to 55%.The introduction of 0.8% Mg-Sn alloy to a blend of 55% cenosphere and 44.2% aluminum resulted in a density increment to 2.14 g/cm 3 .Concurrently, the Vickers hardness exhibited an increase from 37 HV to 53 HV with a rising cenosphere concentration and further escalated to 57 HV upon the addition of the Mg-Sn alloy.Tribological testing revealed that the friction coefficient diminished from 0.293 to 0.235 µ with an escalated cenosphere volume from 25 to 55%.The integration of 0.4% Mg and 0.4% Sn alloy was demonstrated to significantly enhance the friction behavior compared to the pure aluminum and aluminum-cenosphere composites at a 55% volume fraction.The wear rate exhibited a pronounced decrease from 1.957 × 10 -6 to 1.1245 × 10 -6 g/cm under a 10 N load, which correlated with the increasing cenosphere content.This trend persisted under 20 N and 30 N loads, where wear rates diminished with a higher cenosphere volume fraction.The composition comprising 55% cenosphere and 0.8% Mg-Sn alloy manifested the lowest wear rates across varying stress conditions.Compression testing underscored a consistent decrease in compressive strength from 160 MPa to 65 MPa as the cenosphere content rose from 0 to 55%.However, the composite with 55% cenosphere sees a dramatic rise in compressive strength when Mg and Sn are introduced at 0.8 vol.%.Observed changes in density, hardness, friction and wear rates indicate that composite properties can be improved.These composites have the ability to combine mechanical strength with lightweight design, making them attractive for industrial applications.
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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) | 0.000 | 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) | 0.000 | 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 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".