Green Reinforcement: Enhancing Aluminum-Based Composite Manufacturing with Waste Bagasse via Stir Casting Technique
Bibliographic record
Abstract
This study explores the use of waste bagasse ash as reinforcement in aluminum-based composites manufactured via stir casting. Bagasse ash particles were methodically introduced into molten aluminum at 700°C while being stirred at 500 rpm for 12 minutes to achieve uniform dispersion. The addition of 7.5% waste bagasse ash resulted in significant improvements across multiple mechanical properties. Tensile strength increased by 12.45%, hardness showed a remarkable enhancement of 21.32%, fatigue strength exhibited a substantial improvement of 19.45%, and wear resistance demonstrated a notable enhancement of 18.76%, all compared to the base composite. These findings highlight the effectiveness of utilizing waste bagasse ash as reinforcement, offering a sustainable approach to enhance the mechanical properties of aluminum-based composites. This research contributes to advancing eco-friendly manufacturing practices and underscores the potential of waste materials in optimizing material performance.
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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".