Study on microstructure and tribological properties of hierarchical <scp>3D</scp> braid applicable in heavy operating tribology conditions
Bibliographic record
Abstract
Abstract The phenomenon of wear and friction in a variety of environments, including dry, seawater, and high temperatures, has always been a source of contention. Designing and fabricating parts that can function optimally in these conditions, such as self‐lubricating composites, is helpful. This work investigated the tribological and thermal properties of a three‐dimensional braid composite consisting of PTFE yarns and three types of reinforcement in the hierarchical structure under different operating conditions. In this study, the effect of different speeds was considered as well as effect of load. Roughness (Rq) and wear rate of the worn surface were also taken into account while evaluating the friction coefficient. By the response surface method (RSM) and D‐optimal design, the tests were planned experimentally so that the minimum test was performed. Statistical models from the analysis of experimental data by RSM revealed that the friction coefficient of all composites rises in dry conditions with increasing load and sliding speed. In contrast, in seawater conditions, the more difficult the test conditions, the friction took a positive trend. Glass fiber reinforced composites were temperature sensitive and behaved differently in different conditions. The most optimal cases were calculated and reported by the utility function. Furthermore, the coefficient of friction values obtained in all cases were desirable and reliable.
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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 | 0.000 | 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".