Sliding-friction wear of a seashell particulate reinforced polymer matrix composite: modeling and optimization through RSM and Grey Wolf optimizer
Bibliographic record
Abstract
In this work we investigated the friction wear of a thermoplastic polymer (nylon-6) reinforced with particles of seashell. Seashells are made of calcium carbonate in the mineral form of calcite or aragonite, which makes them one of the most robust materials known. A twin-screw extruder was used to blend the seashell particles (SPs) and nylon-6, and an injection molding machine was used to fabricate the composite. Various proportions of SPs (12%, 15%, and 18%, by weight) were added to the nylon-6. We studied the wear of the polymer composites as per the standard ASTM G99, focusing on the loss of material due to wear, the friction coefficient, and the interface temperature. We used a response surface methodology (RSM) based Box–Behnken method (BBD) for our experimental design, and multiobjective analyses were performed incorporating desirability analysis. Our results show the following: the interface temperature was highly influenced by rotational speed (41.61%); the reinforcement with SPs (%) significantly (35.71%) affected the loss of material due to wear; and, the coefficient of friction (CoF) was significantly affected by rotational speed (41.48%) and reinforcement with SPs (18.18% w/w). A novel metaheuristic algorithm (Grey Wolf Optimizer) was used to constrain our optimizations, and the results showed that with CoF = 0.3 and an interface temperature of 25 °C as constraints, the loss due to wear was 35.77 μm for 15.09% w/w reinforcement with SPs, but at CoF = 0.3 and an interface temperature of 30 °C, the loss due to wear was 28.99 μm for 18% w/w reinforcement with SP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".