On the Standardization of Fretting Fatigue Test Method—Modeling Issues Related to the Thermal Constriction Phenomenon and Prediction of Contact Temperature
Bibliographic record
Abstract
The temperature field in the contact zone has a significant effect on the material microstructure, its properties, the oxidation process, and the thermal contact stresses. To standardize fretting fatigue tests, one has to be able to predict and control the contact temperature. Since direct temperature measurement is practically impossible, analytical models are required to estimate the friction-induced temperatures rise under fretting conditions. The main objective of the present work is to model the thermal constriction phenomenon in fretting fatigue and wear processes, considering the roughness and waviness of contacting surfaces. These asperity-scale models can be combined with large scale analyses, e.g. finite element method, to account for the thermal characteristics of the whole tribo-system, its boundary conditions, as well as the spatial variation in the slip amplitude and coefficient of friction over the interface. The debatable question on whether the contact temperature in fretting fatigue is significant is addressed, considering a wide range of materials and applied loads. The analysis showed that the randomness of the contact size may substantially increase the micro-constriction impedance of the fretting interface. The paper is concluded with recommendations for future work to experimentally validate these models, and to examine the effect of the spatial maldistribution of the micro-contacts, and the effect of surface oxide on the contact temperature prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".