On the tribology of complex 2D/3D composites bearing
Bibliographic record
Abstract
In the present work, a hierarchical braided polymer composite consists of Polytetrafluoroethylene (PTFE) fibers, reinforcement fibers, and epoxy resin was designed as a self-lubricant composite and bearing. Different reinforcements such as glass, carbon, and Kevlar fibers were employed to investigate the effect of reinforcement on the wear characteristics of composites. Besides, the influence of 2D/3D braid fabric was examined on the wear behavior of samples. Also, 90 and 120 N loads were applied to assess the load impact. Results illustrated that compared to glass and Kevlar, carbon could lead to a greater wear weight loss and friction coefficient. However, PTFE tribofilm was observed according to scanning electron microscopy (SEM)/energy-dispersive X-ray spectroscopy (EDS) analysis. Moreover, with the increase of load, the wear stability of the composites faded significantly. Furthermore, the tribological features became inappropriate with the deployment of the 3D braid structure. Finally, the modeling of tribological parameters was carried out using response surface methodology-based D-optimal design. The adequacy of the models was checked by analysis of variance. Results implied that there is an excellent correlation between the model and the experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".