Influence of surface residual stresses on the fatigue life and crack propagation behavior of turned Inconel 718 super-alloy
Bibliographic record
Abstract
The effects of surface roughness parameters and residual stresses induced by finish turning on fatigue life and crack propagation behavior of Inconel 718 super-alloy are investigated. A criterion serving as a good indicator of surface quality is also discovered. This is carried out for some specific turning conditions in order to control surface characteristics including surface roughness and the bi-axial state of surface residual stresses and to investigate the effects of these characteristics on fatigue behavior. Rotating bending fatigue (RBF) tests are performed on the machined specimens at room temperature. Characterization techniques including X-ray diffraction (XRD), laser confocal microscopy, and opto-digital microscopy are used to investigate surface residual stresses in axial (ARS) and hoop (HRS) directions, surface roughness, and secondary fatigue cracks, respectively. The huge dispersion in fatigue life of smooth turned Inconel 718 with very low arithmetic average roughness (Ra) values indicates that surface finishing is not the only reliable criterion for quality control of machined components. Multivariate statistical analyses show that axial surface residual stress is the most important surface characteristic affecting the fatigue life of machined Inconel 718 for the conditions tested. Changing ARS from compressive to tensile increases the number of secondary cracks and also decreases the fatigue life of turned specimens.
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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".