Optimization and Performance Analysis of Brake Friction Composites with Pineapple Leaf Fiber and Vermiculite, pp. 97-111
Bibliographic record
Abstract
The growth of industry has caused a rise in the need for materials that create friction.This has led to significant environmental problems at every stage of their existence.As a result, there is now a strong emphasis on creating brake friction materials that are environmentally friendly and sustainable.This study investigates the mechanical and tribological characteristics of Pineapple Leaf Fiber (PLF), a natural fiber, and vermiculite, an industrial waste, as environmentally acceptable additives to improve the behaviour of braking friction composites.Four samples were created, each having varying ratios of vermiculite and PLF.These samples were then evaluated using a friction testing equipment with adjustable speed.The results indicate that the utilization of the environmentally friendly alternative combination can significantly enhance the friction coefficient, minimize friction variations and thermal deterioration.However, it should be noted that the wear rate will also proportionally rise as a result.Furthermore, the deteriorated structure provides evidence of the creation of the contact platform and the process by which wear occurs.The study utilized a hybrid integration of CRITIC (criteria importance through inter-criteria correlation) and multi-objective optimization by ratio analysis (CODAS) to objectively weigh different assessment indicators and rank the samples.The sample containing 6% PLF (polytetrafluoroethylene) and 8% vermiculite demonstrated superior overall tribological performance.
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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 | 0.001 | 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 | 0.000 |
| 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".