Influence of Fiber Direction on the Tribological Behavior of Carbon-Reinforced PEEK at Elevated Temperature for Application in Gas Turbine Engines
Bibliographic record
Abstract
Abstract The aerospace industry aims for net-zero greenhouse gas emissions by 2050, requiring gas turbine engines to reduce CO2 emissions. This will impact engine material selection due to harsher operating conditions, limiting traditional metal/alloy use. While fiber-reinforced polymer (FRP) composites are commonly used in the aerospace industry, their use in gas turbine engines is often restricted by the lower operating temperatures of the polymer matrix. However, many studies have demonstrated the tribological potential of FRP in the fan section of the engines, but little attention has been given to the potential of orienting the fibers in the normal (i.e., out-of-plane) direction relative to the wear surface to leverage the anisotropic properties of FRP composites. This study investigates the impact of fiber orientation on the tribological properties of carbon fiber/polyetheretherketone (PEEK) (CF-PEEK) at elevated temperatures. Three CF-PEEK samples with different fiber orientations were selected for this study (parallel, antiparallel, and normal directions), as well as a fourth sample of pure PEEK. Tribological tests were conducted using a ball-on-disk tribometer at an elevated temperature of 200 °C to evaluate wear and friction behavior. The worn surfaces and counterfaces were analyzed using confocal laser scanning microscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). The findings reveal that CF-PEEK with fibers oriented in the normal direction demonstrates significantly enhanced tribological performance at elevated temperatures, achieving a 95% reduction in the friction coefficient and a 92% decrease in the wear-rate compared to pure PEEK. A wear mechanism has been proposed to explain the superior wear resistance of normally oriented fibers in CF-PEEK, linking it to the development of a fiber-based interface during the run-in phase and the formation of a uniform transfer film.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".