Full-Range Fatigue Life Prediction of Metallic Materials Using Tanaka-Mura-Wu Model
Bibliographic record
Abstract
<div>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.</div>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".