Evaluation of Rolling Contact Fatigue of a Carburized Wind Turbine Gear Considering the Residual Stress and Hardness Gradient
Bibliographic record
Abstract
Carburized gears are applied extensively in large-scale heavy duty machines such as wind turbines. The carburizing and quenching processes not only introduce variations of hardness from the case to the core but also generate a residual stress distribution, both of which affect the rolling contact fatigue (RCF) during repeated gear meshing. The influence of residual stress distribution on the RCF risk of a carburized wind turbine gear is investigated in the present work. The concept of RCF failure risk is defined by combining the local material strength and the multi-axial stress condition resulting from the contact. The Dang Van multi-axial fatigue criterion is applied. The applied stress field is calculated through an elastic-plastic contact finite element model. Residual stress distribution and the hardness profile are measured and compared with existed empirical formula. Based upon the Pavlina–Tyne relationship between the hardness and the yield strength, the gradient of the local material strength is considered in the calculation of the RCF failure risk. Effects of the initial residual stress peak value and its corresponding depth position are studied. Numerical results reveal that compressive residual stress (CRS) is beneficial to RCF fatigue life while tensile residual stress (TRS) increases the RCF failure risk. Under heavy load conditions where plasticity occurs, the accumulation of the plastic strain within the substrate is significantly affected by the initial residual stress distribution.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".