Mechanical and Tribological Behaviours of Aluminium Metal Matrix Composite Reinforced with Bamboo Powder and Iron Filings
Bibliographic record
Abstract
Metal Matrix Composites have wide range of applications in different industries.Conventional materials have limitations when compared to composite materials as they may be lacking in some properties.Metal matrix composites, in particular, offer significant potential because of their improved mechanical and tribological characteristics such as tensile strength, hardness, toughness, and wear resistance.The research aims to produce aluminium 6061 metal matrix composites by incorporating bamboo powder and iron fillings as reinforcements, by so doing, SDG targets 9 and 11 will be met.An efficient and economical approach for producing the composites is crucial.The reinforcement can be readily included into the melt utilizing the costeffective and readily accessible stir casting procedure.Hardness test, wear test, and impact strength tests were conducted on samples produced using the stir casting technique for comparison.From the results, sample 4 (15% iron fillings) had the highest hardness number value and impact energy value and had the lowest wear rate value.Then sample 1 (15% bamboo powder) gave the least impact energy value.Sample 5 (control) gave the least hardness number value followed by sample 1 (15% bamboo powder).Lastly, sample 2 (5% iron fillings and 10% bamboo powder) gave the highest wear rate and sample 4 (15% iron fillings) had the least wear rate value, meaning it has the highest wear resistance value.The result obtained will be useful in the interdisciplinary fields such as materials science, mechanical engineering and structural engineering as well as materials sustainability.
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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 | 0.001 | 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".