Subsurface Fatigue Crack Initiation and Propagation Behavior of Induction-Hardened Shafts under the Effect of Residual and Applied Bending Stresses
Bibliographic record
Abstract
The Society of Automotive Engineers Fatigue Design and Evaluation (SAEFDE) Committee has been conducting a long-term program aimed at the development of a predictive capability for fatigue life of SAE 1045 induction-hardened shafts. As a part of a larger-scale investigation provided by the SAEFDE committee, this research provided an analytical model capable of predicting the total fatigue life, both crack initiation and crack propagation, of an induction-hardened shaft under applied bending stress. The analysis procedure incorporated the effects of residual stresses. Total stress intensity factors were calculated and superimposed using applied bending stress intensity factors and residual stress intensity factors along the subsurface elliptical crack front. Fatigue tests were conducted using SAE 1045 induction-hardened shafts to verify the analytical models of subsurface fatigue crack growth. The total fatigue life calculations of subsurface failure showed a factor from 0.6 to 0.8 compared with the experimental results. The analytical model and experimental data confirmed that the majority of the total fatigue life is spent in the crack propagation phase.
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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".