Taguchi Optimization of Wear Rate for Hypoeutectic Al-Si Alloy Through Al2O3 and SiC Addition
Bibliographic record
Abstract
Aluminum silicon alloy (Al-9% Si) was fabricated by using a metal mold casting.The alloy was reinforced with two types of ceramic materials, 5% Al2O3 (alumina) and 5% SiC (silicon carbide).A wear test was performed with pin on disc wear device.Three parameters were used; applied load, time, and type of material.The microstructures were examined by using an optical microscope.It was found that the additional ceramic particle materials increased the wear resistance and hardness values.However, the effect of adding SiC particles to the alloy on wear resistance and hardness was higher than the addition of Al2O3 particles to the same alloy.An experimental plan via Taguchi's technique has been utilized for conducting an L9 orthogonal array.Analysis of variance (ANOVA) has been utilized to find optimal wear rate under the impact's parameters of applied load, time, and type of material.Wear resistance of dry sliding has been analyzed according to the rule of "smaller the best."The final study of the impact parameters showed the loading being applied has the maximum impact on the rate of wear resistance compared to time and followed by the type of material.
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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) | 0.001 | 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".