Advancing Aluminum-Based Composite *Manufacturing: Leveraging TiO2 Reinforcement through Stir Casting Technique
Bibliographic record
Abstract
This study explores the advancement of aluminum-based composite manufacturing through the integration of titanium dioxide (TiO2) reinforcement using the stir casting technique. Aluminum alloy served as the matrix material, combined with ceramic reinforcement particles, melted at approximately 700°C within a muffle furnace. Through continuous stirring at 400 rpm for 10 minutes, ceramic particles were uniformly dispersed into the molten alloy, crucial for enhancing composite properties. The incorporation of 6.5% TiO2 via stir casting resulted in significant enhancements across multiple mechanical properties. Tensile strength improved by 23.67%, while hardness saw a remarkable increase of 38.9%. Additionally, fatigue strength exhibited a notable improvement of 26.67%, and wear resistance showed a substantial enhancement of 24.34%. The uniform dispersion of TiO2 particles throughout the composite material underscores the efficacy of the stir casting technique in achieving consistent improvements across various performance metrics. These findings hold promise for the development of high-performance aluminum-based composites tailored for diverse engineering 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 | 0.001 | 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 | 0.001 |
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".