Effectiveness of Shannon Entropy Weight Method on Wear Behaviour of Polyester/Carbon Fibre Composites Using GRA
Bibliographic record
Abstract
In recent past conventional monolithic materials are replaced with fiber reinforced polymer composite materials due to their high specific strength. The current study focused on dry-sliding wear behaviour of carbon fiber reinforced polyester (CFRP) composites using a pin-on-disc tribometer. The two output responses selected were rate of wear and frictional force with respect to controlled variables using the Taguchi L16 OA (Orthogonal Array). In order to assess the best optimal conditions GRA technique has been used in the study. The effectiveness of entropy weights on the optimal result has been carried out in support with ANOVA studies. In GRA analysis, the combined effect of wear and frictional force is considered and the optimal conditional identified in two ways namely equal weightage method (EWM) and entropy based weightage method (EBWM). While considering EWM method the optimal condition obtained is S1 L4 D3 R4 whereas in EBWM the optimal solution obtained is S1 L4 D1 R4. This shows because of the uneven weights generated by EBWM method there is a change in optimal solution in comparison with EWM method.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".