Application of Taguchi – PCA/GRA Method to Optimize the Wear Behaviour of Polyester/Carbon Fibre Composites
Bibliographic record
Abstract
Fibre-reinforced polymer composites have begun to replace the conventional monolithic materials in the recent years as a result of better specific strength and enhanced characteristics.The present work has put forward an attempt to study the dry-sliding wear behaviour of CFRP composites followed by implementation of Taguchi-GRA combinatorial approach for the two output responses namely, wear and frictional force.Load (L), sliding distance (D), sliding velocity (S), and percent of fibre reinforcement (R) are considered as input conditions and the experiments were planned using design of experiments.In addition, Principal Component Analysis (PCA) has taken into consideration for the weights calculation in GRA.An elaborate study on the implementation these methodologies on the combined behaviour of wear and frictional force has been presented.Furthermore, ANOVA data is analysed, it appears that both the load (L) and the percent reinforcement (R) has greater effect towards the wear behaviour.A detailed discussion on wear mechanism has also been presented with a support of SEM morphology.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".