Optimization and Tribological Properties of Hybridized Palm Kernel Shell Ash and Nano Boron Nitride Reinforced Aluminium Matrix Composites
Bibliographic record
Abstract
The tribological properties of hybridized reinforced aluminium matrix composites were optimized using Taguchi and Grey Relational Analysis in conjunction with an L16 orthogonal array. The combination of palm kernel shell ash (PKSA) (0–5 wt. %) along with nano BN reinforcements was taken in interest. Loads and speeds (500, 750, 1000, and 1250 rpm) were employed as control parameters for the experiment. Using a Taber type abrasion machine, the wear samples were made, and the wear experiments were carried out. Speed and load were more important than the percentage of reinforcements in composites when it came to evaluating wear index and loss of volume. With respect to wear index and volume loss, Taguchi‐relational Grey’s analysis identified A3B1C1 (reinforcement = 5 wt. %; load = 500 g; speed = 500 rpm) as the optimal process parameter combination, with a reinforcement of 3 wt. %, load = 500 g, and speed = 500 rpm being the second‐best option. Validation tests have revealed that the anticipated and experiment values at the optimal situations are both within the acceptable range. Performance is influenced more by speed than by load, which is influenced more by the weight percentage of composites, as demonstrated by the application of the Taguchi and Grey Relational Analysis methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".