Full-Range Fatigue Life Prediction of Metallic Materials Using Tanaka-Mura-Wu Model
Bibliographic record
Abstract
In this research, the recently developed Tanaka-Mura-Wu (TMW) model is applied to common engineering materials including Ni-base superalloys Haynes 282 and Inconel 617, aluminum alloys 7075-T6 and 2024-T3, alloy steels SAE 4340 and SAE 1020, and titanium alloy Ti-6Al-4V, as well as a high-entropy alloy (HEA) CoCrFeMnNi over the full fatigue range comprised of low-cycle fatigue (LCF) and high-cycle fatigue (HCF). Through the analysis, it is shown that the TMW model is able to provide class A prediction for LCF (forecast before the event occurs) without resorting to fatigue testing; and with calibration at one stress level, it can be extended to the HCF regime. A relationship of fatigue life versus the total strain is established with the use of the Ramberg-Osgood equation. The TMW model predictions agree well with the experimental data and/or the Coffin-Manson-Basquin relation for the above materials. The TMW model describes the full-range fatigue life in terms of material’s elastic modulus, Poisson’s ratio, surface energy, and the Burgers vector. Thus it establishes a physics-based baseline for characterizing the effects of other contributing factors such as microstructure and surface roughness, which contribute to the uncertainty in the fatigue scatter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".