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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".