Study on mechanical, tribological and fracture behaviour of n-Al <sub>2</sub> O <sub>3</sub> reinforced Al7075 composites using Taguchi technique
Bibliographic record
Abstract
The work focuses on producing and investigating the mechanical, wear, and microstructure of Al7075 alloy nano-sized Alumina Oxide (n-Al2O3) particles with wt. % of 1, 2, and 3. Metallurgical methods have been used to create Al7075/n-Al2O3 composites. The microstructure showed that n-Al2O3 was distributed uniformly. Tests were done on a tribometer that was fastened to a hard steel disk to examine wear loss. In response to the surface approach, the wear parameters were optimised using the Taguchi L27 orthogonal array. The obtained result indicates that, hardness and tensile strength increase by 40% & 31% respectively. It is due to the increase of wt. % hard ceramic nano particulates. The Analysis of Variance (ANOVA) result indicates that, wt. % of n-Al2O3 is the most significant (61.02 %). With 95% reliability, the constructed model successfully predicted the wear rate, & ANOVA was used to corroborate the outcomes of all models.
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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".